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.

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.

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.

Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems.

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1-3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1-30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (0% YD). In Step 3, yield, rumination, and physical activity data from days 1-3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1-3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow 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.

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.

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.

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.

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.

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.

Evaluation of selected risk factors for different stages of digital dermatitis in Dutch dairy cows.

Digital dermatitis (DD) is a painful infectious disease in dairy cattle that causes ulcerative lesions of the skin just above the coronary band, mainly of the hind legs. Estimates for DD prevalence at cow level in the Netherlands range from 20% to 25%. In this study, risk factors for the various stages of DD were identified and quantified. The hind legs of 6766 cows on 88 farms were scored by trained interns, using the M-scoring system (M0-M4.1). Farms in this study were a convenience sample, based on the prevalence of DD as recorded at the latest herd trim, geographical location and willingness of the farmers to participate. A survey with questions about cow environment and herd management was conducted by the intern at the day of scoring. The data were collected between August 2017 and January 2018. DD was found on 38.6% of the scored legs; 49.8% of the cows had DD on at least one leg and M4 was the most frequent stage (20.9%). Not removing manure on a regular basis resulted in lower odds for M2, M4 and M4.1 compared to cleaning by automatic scrapers ten times a day or more (odds ratio [OR]= 0.16, 0.49 and 0.18, respectively). The odds for M2 and M4 lesions were higher in cows aged 3-5 years than in first-calved cows (OR> 1.5 and > 1.7, respectively). Rubber flooring in the passageways resulted in lower odds for both M1 and M2 (OR, 0.06 and 0.32, respectively). Prophylactic use of footbaths treatment with an alternative active compound resulted in significant higher odds for M4 lesions than formalin and a combination of formalin and copper sulphate (OR= 1.69 and 2.04 respectively). The odds for an M4.1 lesion were lower in cows from smaller herds (n = 50-100) compared to large herds (n >100; OR= 0.67).

Biometric identification of dairy cows via real-time facial recognition.

Biometrics methods, which currently identify humans, can potentially identify dairy cows. Given that animal movements cannot be easily controlled, identification accuracy and system robustness are challenging when deploying an animal biometrics recognition system on a real farm. Our proposed method performs multiple-cow face detection and face classification from videos by adjusting recent state-of-the-art deep-learning methods. As part of this study, a system was designed and installed at four meters above a feeding zone at the Volcani Institute's dairy farm. Two datasets were acquired and annotated, one for facial detection and the second for facial classification of 77 cows. We achieved for facial detection a mean average precision (at Intersection over Union of 0.5) of 97.8% using the YOLOv5 algorithm, and facial classification accuracy of 96.3% using a Vision-Transformer model with a unique loss-function borrowed from human facial recognition. Our combined system can process video frames with 10 cows' faces, localize their faces, and correctly classify their identities in less than 20 ms per frame. Thus, up to 50 frames per second video files can be processed with our system in real-time at a dairy farm. Our method efficiently performs real-time facial detection and recognition on multiple cow faces using deep neural networks, achieving a high precision in real-time operation. These qualities can make the proposed system a valuable tool for an automatic biometric cow recognition on farms.

Heritability and genetic correlations of rumination time with milk-yield and milking traits in Holstein-Friesian cows using an automated milking system.

Knowledge of the values of genetic parameters is a prerequisite for conducting a breeding program. This is especially important for rumination, which is considered an indicator of cow's health. Exploring the genetic relations between rumination time, milk yield, and milking traits could make it a valuable tool in dairy cattle breeding strategies. The objective of the research was to estimate heritability, repeatability, and genetic and phenotypic correlations of rumination time (RT), as well as traits associated with milk yield and milking of dairy cows of the Polish Holstein-Friesian breed kept in herds equipped with an automatic milking system. The research takes into consideration daily results for milking in the first lactation and second lactation, from 1 486 cows of the breed milked between 2013 and 2015 year. Cows were housed in 24 free-stall barns and fed a Partial Mixed Ration feed. The barns had an automated milking system (Astronaut A4 - Lely Industry). The cows received a varied dose of the concentrate, either in the milking robot or the feeding station, depending on the level of their milk yield. Our research has shown that RT was a low heritable trait (0.140 ± 0.039) and had a medium repeatability (0.572 ± 0.007). We detected a positive genetic correlation between RT and milk yield (0.341); however, a statistically significant negative relationship was identified between RT and urea content (-0.418) in milk. Estimations of genetic correlations suggest that selecting for higher RT may correspond to reduced urea content in milk. Investigating the genetics aspect of RT and the relationship with milk yield and milking traits may turn this into one of the useful criterion selections for dairy cattle breeding strategies, but should be used carefully. Further analyses on larger data sets and different populations are necessary.

Utilizing 3D Point Cloud Technology with Deep Learning for Automated Measurement and Analysis of Dairy Cows.

This paper introduces an approach to the automated measurement and analysis of dairy cows using 3D point cloud technology. The integration of advanced sensing techniques enables the collection of non-intrusive, precise data, facilitating comprehensive monitoring of key parameters related to the health, well-being, and productivity of dairy cows. The proposed system employs 3D imaging sensors to capture detailed information about various parts of dairy cows, generating accurate, high-resolution point clouds. A robust automated algorithm has been developed to process these point clouds and extract relevant metrics such as dairy cow stature height, rump width, rump angle, and front teat length. Based on the measured data combined with expert assessments of dairy cows, the quality indices of dairy cows are automatically evaluated and extracted. By leveraging this technology, dairy farmers can gain real-time insights into the health status of individual cows and the overall herd. Additionally, the automated analysis facilitates efficient management practices and optimizes feeding strategies and resource allocation. The results of field trials and validation studies demonstrate the effectiveness and reliability of the automated 3D point cloud approach in dairy farm environments. The errors between manually measured values of dairy cow height, rump angle, and front teat length, and those calculated by the auto-measurement algorithm were within 0.7 cm, with no observed exceedance of errors in comparison to manual measurements. This research contributes to the burgeoning field of precision livestock farming, offering a technological solution that not only enhances productivity but also aligns with contemporary standards for sustainable and ethical animal husbandry practices.

[Selective dry cow therapy on dairy farms in Rhineland-Palatinate, Saarland and Hesse, Germany - a survey among farmers]. / Selektives antibiotisches Trockenstellen bei Milchkühen in Rheinland-Pfalz, dem Saarland und Hessen ­ eine Umfrage unter Landwirten.

OBJECTIVE: With the Regulation (EC) 6/2019, antibiotic drying off of the entire dairy herd is no longer permissible. Hence, it is necessary to establish selective antibiotic drying off (SDCT: Selective Dry Cow Therapy) in dairy herds. With the publication of the PraeRi study in 2020, systematic data for the implementation of SDCT on farms became available for several German states. For Rhineland-Palatinate, Saarland and Hesse this type of information is only available from individual projects. Therefore, the aim of this survey was to increase the knowledge concerning the implementation of SDCT in dairy farms located in these states. MATERIAL AND METHODS: An online questionnaire was sent via newsletters to farmers and was published in the regional farmers' bulletins in the described catchment area. The questionnaire inquired about the saving of antibiotics during drying off, the criteria guiding the farmer's decision (cell count from monthly dairy herd improvement data (DHI), mastitis history, microbiological examination of quarter foremilk samples, California mastitis test), use of teat sealants and the type of dry off procedure (abrupt/gradual). RESULTS: A total of 103 questionnaires were evaluated, making the response rate ~1% for Hesse, ~3% for Saarland, and ~5% for Rhineland-Palatinate based on the number of included farms. Approximately 29% of the farmers dried off one out of four cows, 20% half, 23% three out of four and 13% all cows without using antibiotics. Eighty-nine farm managers based their decision on the somatic cell counts of DHI. Additional criteria influencing the decision were the mastitis history, results of the California Mastitis Test, or a combination of both. In 76 farms cows were dried off abruptly. In 79 farms teat sealers were used. CONCLUSIONS: Application of SDCT is established in most of the farms that participated in the survey, even though the proportion varied between farms. Legal requirements are not the only reason farmers need to increasingly deal with SDCT; sustainability programs of the dairies rely on selective drying off as well. Herd veterinarians should be supportive in implementing these measures to achieve good udder health while reducing the use of antimicrobials to a necessary minimum.

Mitigation of gaseous emissions from dairy livestock: A farm-level method to examine the financial implications.

Feeding the world's population while minimising the contribution of agriculture to climate change is one of the greatest challenges facing modern society. This challenge is particularly pronounced for dairy production where the carbon footprint of products and the mitigation costs are high, relative to other food stuffs. This paper reviews a number of mitigation measures that may be adopted by dairy farmers to reduce greenhouse gas emissions from their farms. A simulation model is developed to assess the cost-benefit of a range of mitigation measures. The model is applied to data from Ireland, a country with a large export-oriented dairy industry, for a range of farms including top, middle and bottom performing farms from a profitability perspective. The mitigation measures modelled included animal productivity, grass production and utilisation, better reproductive performance, early compact calving, reduced crude protein, decreased fertiliser N, protected urea, white clover, slurry tank cover and low emission slurry spreading (LESS). The results show that over half of the greenhouse gas abatement potential and most of the ammonia abatement potential were realised with cost-beneficial measures. Animal and feed-related measures that increased efficiency drove the abatement of GHG emissions. Low-emission slurry spreading was beneficial for the bottom and middle one-third of farms, while protected urea and reducing nitrogen use accounted for most of the ammonia abatement potential for the most profitable farms. Results showed that combining mitigation measures resulted in a decrease of 23%, 19%, and 12% in GHG emissions below 2020 levels for the bottom, middle, and top performing dairy farms, respectively. The findings imply that top dairy farms, that are already managed efficiently and optimally, may struggle to achieve the national and international GHG reduction targets with existing technologies and practices.

Farmers´ sense of the biological impact of extreme heat and seasonality on Swedish high-yielding dairy cows - A mixed methods approach.

Supporting dairy farmers in becoming resilient towards extreme weather requires a broad understanding of the experiences and perceived risks associated with these events from those who undergo them. We used a mixed methods approach to explore national trends of biological consequences on dairy cow udder health and fertility, combined with in-depth farmer conversations around extreme weather events, focusing on heat. The aim is to provide a comprehensive picture of how dairy farmer perceptions, priorities and decision-making are related to the season and extreme weather to identify preventive pathways that can reduce biological costs of heat stress on Swedish dairy cattle during summer. Data collected monthly at cow and farm level between 2016-2019 as part of the Swedish milk and disease recording system confirm seasonal trends and show increased somatic cell counts (SCC) and negatively impacted fertility during summers. In addition, transcriptions of 18 interviews with dairy farmers across the country and seasonal variations of SCC and fertility were thematically analysed. The results suggest that farmers have a broad definition of extreme weather and are aware of the negative impacts. Yet handling of extreme weather events can mainly be classified as reactive. Nevertheless, there are long-term effects on the farm economy, health and herd dynamics. Swedish dairy farmers are currently showing resilience, albeit a fragile one. The capability to ensure sufficient feed production in extreme weather is critical for farm self-perceived resilience. However, acknowledging the long-term biological costs related to fertility, currently not perceived by farmers, has the potential to support proactive planning and improve farm resilience and profitability.

[Vibration as a risk of mastitis during milking]. / Vibration als Mastitis-Risiko beim Melken.

INTRODUCTION: Mastitis is one of the most important factor diseases in dairy cattle worldwide. Milking technique represents one of the factors involved in the development of mastitis. The purpose of this study was to investigate the influence of vibrations during milking on the rate of clinical and subclinical mastitis. For this purpose, milking measurements, tank milk analyses and survey forms (general farm data, assessment of milking work and milking hygiene, teat condition, feeding, farm problems, animal behavior) were assessed in 8 Swiss dairy farms. The results show a correlation between present vibrations at the output of the milk meter and increasing bulk milk somatic cell count. Further, a tendency was shown for vibrations at the input of the milk meter to influence bulk milk somatic cell count. Also, a tendency regarding vibrations at the outlet of the milk meter and acute phase protein milk amyloid A was evident. In conclusion, the results suggest that vibration during milking might have a negative effect on udder health. However, further research with a larger number of dairies is needed to make a more generally valid statement.

INTRODUCTION: La mammite est l'une des maladies les plus importantes chez les vaches laitières dans le monde entier. La technique de traite représente l'un des facteurs impliqués dans le développement de la mammite. L'objectif de cette étude était d'étudier l'influence des vibrations pendant la traite sur le taux de mammites cliniques et subcliniques. Pour ce faire, des mesures de traite, des analyses de lait de tank et des formulaires d'enquête (données générales de l'exploitation, évaluation du travail de traite et de l'hygiène de la traite, état des trayons, alimentation, problèmes de l'exploitation, comportement des animaux) ont été évalués dans 8 exploitations laitières suisses. Les résultats montrent une corrélation entre les vibrations présentes à la sortie du compteur à lait et l'augmentation du nombre de cellules somatiques du lait en vrac. En outre, les vibrations à l'entrée du compteur à lait ont tendance à influencer le nombre de cellules somatiques du lait en vrac. De même, une tendance concernant les vibrations à la sortie du compteur à lait et la protéine amyloïde A du lait de phase aiguë a été mise en évidence. En conclusion, les résultats suggèrent que les vibrations pendant la traite pourraient avoir un effet négatif sur la santé de la mamelle. Cependant, des recherches supplémentaires avec un plus grand nombre de laiteries sont nécessaires pour faire une déclaration plus généralement valable.

Informational resources used by farmers with ruminants and monogastrics for animal health monitoring: importance of sensory indicators.

Managers of health in livestock systems are asked to shift from a curative approach to a more preventive approach. This change requires sociological and technical reconfiguration and raises the issue of how changes are implemented by farmers and their technical support ecosystem (advisors, trainers, veterinarians). Here, we report work conducted in western France by an Agricultural European Innovation Partnership Operational Group bringing together animal scientists and sociologists to advance knowledge on animal health in a range of livestock sectors, i.e. dairy cattle, beef cattle, small ruminants (sheep, goats), poultry and pigs. In this study, our aim was to answer this question: what are the Informational Resources (I.R.) that farmers use to promote animal health of their herds? First, we used a survey to characterize 129 I.R. used by advisors, then, we used statistical analysis to classify these I.R. into six clusters. Second, we organized eight focus-group sessions that involved a total of 50 farmers from across all livestock sectors to find out how they mobilize the I.R. and what they see as important for animal health monitoring practice. Finally, we performed individual interviews with 42 farmers to expand the data captured in the collective focus groups. Results showed that farmers and advisors have a broad and diverse range of I.R. to help monitor animal health. We identified six clusters of I.R.: regulatory tools, periodic reports, tools for farmer-led monitoring, tools and indicators for national reference datasets, slaughterhouse and laboratory indicators, and training delivered to farmers. During focus group, livestock farmers identified some of their I.R. within these clusters but they also cited other daily routines that help them monitor animal health that were not cited by advisors. We found that farmers mainly use sensory indicators (typically smell, sight, touch) in their daily practice whereas advisors mainly use relatively sophisticated retrospective monitoring tools. Farmers also cited the importance of indicators that can rapidly objectify any change in animal condition, behavior, or health. This work finds a split in the distribution of animal health management roles, with farmers implementing daily checks whereas advisors run periodic health surveillance, thus revealing differentiated roles and needs between farmers and their advisors.

Assessing the environmental impacts of beef production chains integrating grazing and landless systems.

Livestock production systems contribute significantly to environmental impacts at the global level, and meat consumption is projected to increase with the population. There is a need to reduce the impact of food production, including that from beef systems. Different production systems, ranging from traditional grazing to landless systems, coexist within the beef sector. Among these, mixed systems have emerged as a promising alternative. These mixed systems typically involve adult cattle in grazing systems alongside fattening calves in landless systems, potentially achieving higher productivity while reducing the overall environmental impacts. The first step towards proposing mitigation strategies involves identifying the impacts of the sector. This study aimed to estimate the main environmental impacts of four types of mixed beef systems based on the origin of the calves that are raised, fattened, and slaughtered. Using life cycle assessment, the study evaluated the environmental impacts from the cradle to the slaughterhouse gate, expressed per kilogram of carcass weight. The four systems assessed include suckler cow farms that fatten their own offspring (beef single farm, BSF), a system in which calves raised on a suckler farm are fattened on a different farm (beef fattening unit, BFU), and systems in which dairy calves are fattened on growing units, with calves either from Spain (dairy national, DN) or from farms located abroad (dairy abroad, DA). Primary data were obtained from representative surveys of farmers and slaughterhouses, and allocation between co-products was performed according to the updated guidelines of Environmental Product Declarations and the Product Category Rules for meat. Seven environmental impact categories were assessed: climate change, marine eutrophication, freshwater eutrophication, stratospheric ozone depletion, terrestrial acidification, photochemical ozone formation on ecosystems, and photochemical ozone formation on human health. The results indicate that meat production from BSF and BFU has greater environmental impacts than that from DN and DA systems, primarily due to the lower environmental burden allocated to dairy calves, whereas the contribution of slaughterhouse activities to the environmental impacts was minimal. This study highlights the importance of mitigating the environmental impacts associated with feed production, enteric fermentation, and manure management in beef systems. Future studies should consider potential environmental benefits of grazing animals such as carbon sequestration and biodiversity promotion.