Application of behavior data to predictive exploratory models of metritis self-cure and treatment failure in dairy cows

The objective was to evaluate the performance of exploratory models containing routinely available on-farm data, behavior data, and the combination of both to predict metritis self-cure (SC) and treatment failure (TF). Holstein cows (n = 1,061) were fitted with a collar-mounted automated-health monitoring device (AHMD) from -21 ± 3 to 60 ± 3 d relative to calving to monitor rumination time and activity. Cows were examined for diagnosis of metritis at 4 ± 1, 7 ± 1, and 9 ± 1 d in milk (DIM). Cows diagnosed with metritis (n = 132), characterized by watery, fetid, reddish/brownish vaginal discharge (VD), were randomly allocated to 1 of 2 treatments: control (CON; n = 62), no treatment at the time of metritis diagnosis (d 0); or ceftiofur (CEF; n = 70), subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid on d 0 and 3 relative to diagnosis. Cure was determined 12 d after diagnosis and was considered when VD became mucoid and not fetid. Cows in CON were used to determine SC, and cows in CEF were used to determine TF. Univariable analyses were performed using farm-collected data (parity, calving season, calving-related disorders, body condition score, rectal temperature, and DIM at metritis diagnosis) and behavior data (i.e., daily averages of rumination time, activity generated by AHMD, and derived variables) to assess their association with metritis SC or TF. Variables with P-values ≤0.20 were included in the multivariable logistic regression exploratory models. To predict SC, the area under the curve (AUC) for the exploratory model containing only data routinely available on-farm was 0.75. The final exploratory model to predict SC combining routinely available on-farm data and behavior data increased the AUC to 0.87, with sensitivity (Se) of 89% and specificity (Sp) of 77%. To predict TF, the AUC for the exploratory model containing only data routinely available on-farm was 0.90. The final exploratory model combining routinely available on-farm data and behavior data increased the AUC to 0.93, with Se of 93% and Sp of 87%. Cross-validation analysis revealed that generalizability of the exploratory models was poor, which indicates that the findings are applicable to the conditions of the present exploratory study. In summary, the addition of behavior data contributed to increasing the prediction of SC and TF. Developing and validating accurate prediction models for SC could lead to a reduction in antimicrobial use, whereas accurate prediction of cows that would have TF may allow for better management decisions.

Farm-level nutritional factors associated with milk production and milking behavior on Canadian farms with automated milking systems

The objective of this study was to describe the nutritional strategies used on Canadian dairy farms with automated milking systems (AMS), both at the feed bunk and the concentrate offered at the AMS, as well as to determine what dietary components and nutrients, as formulated, were associated with milk production and milking behaviors on those farms. Formulated diets (including ingredients and nutrient content) and AMS data were collected from April 1, 2019, until September 30, 2020, on 160 AMS farms (eastern Canada [East] = 8, Ontario [ON] = 76, Quebec [QC] = 22, and western Canada [West] = 54). Both partial mixed ration (PMR) and AMS concentrate samples were collected from May 1 to September 30, 2019, on 169 farms (East = 12, ON = 63, QC = 42, West = 52). We collected AMS milking data for 154 herds. For each farm (n = 161), milk recording data were collected and summarized by farm to calculate average milk yield and components. Multivariable regression models were used to associate herd-level formulated nutrient composition and feeding management practices with milk production and milking behavior. Milk yield (mean ± SD = 37.0 ± 0.3 kg/d) was positively associated with the PMR ether extract (EE) concentration (+0.97 kg/d per percentage point [p.p.] increase) and with farms that fed barley silage as their major forage source (n = 16; +2.18 kg/d) as compared with haylage (n = 42), whereas farms that fed corn silage (n = 96; +1.23 kg/d) tended to produce more milk than farms that fed haylage. Greater milk fat content (4.09 ± 0.28%) was associated with a greater PMR-to-AMS concentrate ratio (+0.02 p.p. per unit increase) and total diet net energy for lactation (+0.046 p.p. per 0.1 Mcal/kg increase), but a lesser percentage of NFC of the PMR (-0.016 p.p. per p.p. increase of NFC percentage). Milk protein content (3.38 ± 0.14%) was positively associated with the forage percentage of the PMR (+0.003 p.p. per p.p. increase of forage percentage) and the total diet starch percentage (+0.009 p.p. per p.p. increase of starch percentage), but was negatively associated with farms feeding corn silage (-0.1 p.p. compared with haylage) as their major forage. Greater milking frequency (2.77 ± 0.40 milkings/d) was observed on farms with free-flow cow traffic systems (+0.62 milkings/d) and was positively associated with feed push-up frequency (+0.013 milkings/d per additional feed push-up), but negatively associated with PMR NFC content and forage percentage of the total ration (-0.017 milkings/d per p.p. increase of forage percentage). Lastly, greater milking refusal frequency (1.49 ± 0.82 refusals/d) was observed on farms with free-flow cow traffic systems (+0.84 refusals/d) and farms feeding barley silage (+0.58 refusals/d) than with guided flow and farms feeding either corn silage or haylage, respectively. These data give insight into the ingredients, nutrient formulations and type of diets fed on AMS dairy farms across Canada and the association of those factors with milk production and milking behaviors.

Intelligent Animal Husbandry: Present and Future

The main priorities in the contemporary breeding of different animal species have been directed toward the use of intelligent approaches for accelerating genetic progress, ensuring animal welfare and environmental protection by reducing the release of manure and gas emissions [...].

Randomized controlled study assessing the effect of milking permission settings and concentrate supplementation on milking frequency and milk yield in a pasture based automatic milking system

This study aimed to verify the impact of milking permission (MP) and concentrate supplementation (CS) on milking frequency (milkings per cow/day) and milk yield (kg per cow/day) in a farm using a pasture-based automatic milking system (AMS). Sixty-eight cows milked using this AMS unit were randomly assigned to one of 4 groups homogeneous for parity, days in milk and milk yield. Treatments used were: Frequent (F) or Restricted (R) MP, that granted cows permission to milk after 6 to 8 h or 9.6 to 14 h of the previous milking, respectively; and low (LC) or high (HC) CS of 0.5 kg or 3.5 kg per cow/day, respectively. The combination of the 2 levels of MP and the 2 levels of CS resulted in the 4 treatment combinations (FHC, RHC, FLC, RLC). This study was designed as a 2 X 2 factorial arrangement with treatment crossover: each of the 4 cow-groups was randomly assigned to one of the 4 treatment combinations for a 5-week experimental period (one pre-treatment week and 4 treatment weeks), and after each 5-week period groups crossed over to another treatment combination until they experienced all. Statistical analysis assessed the impact of MP, CS and their interaction on milk yield, milking frequency, box time, milking time and average milk flow rate. This was done using a mixed model analysis with repeated measures to account for repeated observations on the experimental unit (cow). Milk yield per cow/day and milkings per cow/day were significantly higher with the Frequent compared with the Restricted MP (1.5 kg and 0.65 respectively). Milk yield per cow/day and milkings per cow/day were significantly higher with the HC compared with the LC CS (3.1 kg and 0.25 respectively). Additionally, milk yield per cow/day was affected by the interaction of MP and CS and it was highest with the FHC (20.1 kg) treatment combination, followed by RHC (18.2 kg) treatment combination. The number of milkings per cow/day were also affected by the interaction of MP and CS. The highest estimated number of milkings per cow/day was recorded for the FHC (2.12) and the FLC (1.77) treatment combinations, followed by the RHC (1.38) and RLC (1.23) treatment combinations. Similarly, milking interval was 2.5 h longer for the RLC treatment combination compared with RHC. The shortest milking interval/milking was observed for the FHC (11 h) and FLC (12.8 h) treatment combinations. In conclusion, the study showed that allowing access to the robot between 6 to 8 h after the previous milking was sufficient (even with a minimal level of CS) to achieve acceptable milk production and milking performance in a pasture-based AMS.

Milking system and diet's forage type impact on milk quality of Italian Holstein-Friesian

Moving from conventional (CMS) to automatic (AMS) milking systems could impact milk quality. Moreover, the type and preservation methods of the forages used in the total mixed ration (TMR) (such as alfalfa hay -HTMR- or corn silage -STMR-) have been demonstrated to modify milk composition. Thus, this study investigated the effect of implementing AMS and different diet forage types on the quality of Italian Holstein-Friesian bulk milk. Milk samples (n = 168) were collected monthly from 21 commercial farms in northern Italy during a period of 8 mo. Farms were categorized into 4 groups according to their milking system (CMS vs AMS) and diet's forage type (HTMR vs STMR). Milk quality data were analyzed through the mixed procedure for repeated measurement of SAS with the milking system, diet's forage type, and sampling day as fixed effects. Milking through the AMS led to lower milk fat, freezing point and β-lactoglobulin A, longer coagulation time, and higher K content, pH and β-lactoglobulin B than CMS. Cows fed STMR produced milk with greater fat, protein, casein, Mg content, titratable acidity and β-lactoglobulin A, while reduced curd firming time, freezing point and β-lactoglobulin B than those fed with HTMR. In conclusion, milk quality is not only altered by the diet's forage type and characteristics but also by the milking system.

Farm-level risk factors associated with increased milk β-hydroxybutyrate and hyperketolactia prevalence on farms with automated milking systems

The objectives of this study were to determine the farm-level hyperketolactia (HKL) prevalence, as diagnosed from milk β-hydroxybutyrate (BHB) concentration, on dairy farms milking with an automatic milking system (AMS) and to describe the farm-level housing, management, and nutritional risk factors associated with increased farm-average milk BHB and the within-herd HKL prevalence in the first 45 DIM. Canadian AMS farms (n = 162; eastern Canada n = 8, Quebec n = 23, Ontario n = 75, western Canada n = 55) were visited once between April to September 2019 to record housing and herd management practices. The first test milk data for each cow under 45 DIM were collected, along with the final test of the previous lactations for all multiparous cows, from April 1, 2019 to September 30, 2020. The first test milk BHB was then used to classify each individual cow as having HKL (milk BHB ≥ 0.15 mmol/L) at the time of testing. Milk fat and protein content, milk BHB, and HKL prevalence were summarized by farm and lactation group (all, primiparous, and multiparous). During this same time period, formulated diets for dry and lactating cows, including ingredients and nutrient composition, and AMS milking data were collected. Data from the AMS were used to determine milking behaviors and milk production of each herd during the first 45 DIM. Multivariable regression models were used to associate herd-level housing, feeding management practices, and formulated nutrient composition with first test milk BHB concentrations and within-herd HKL levels separately for primiparous and multiparous cows. The within-herd HKL prevalence for all cows was 21.8%, with primiparous cows having a lower mean prevalence (12.2 ± 9.2%) than multiparous cows (26.6 ± 11.3%). Milk BHB concentration (0.095 ± 0.018 mmol/L) and HKL prevalence for primiparous cows were positively associated with formulated prepartum DMI and forage content of the dry cow diet while being negatively associated with formulated postpartum DMI, the major ingredient in the concentrate supplemented through the AMS, and the PMR-to-AMS concentrate ratio. However, multiparous cows' milk BHB concentration (0.12 ± 0.023 mmol/L) and HKL prevalence were positively associated with the length of the previous lactation, milk BHB at dry off, prepartum diet nonfiber carbohydrate content, and the major forage fed on farm, while tending to be negatively associated with feed bunk space during lactation. This is the first study to determine the farm-level risk factors associated with herd-level prevalence of HKL in AMS dairy herds, thus helping optimize management and guide diet formulation to promote the reduction of HKL prevalence.

Genetic parameters for udder conformation traits derived from Cartesian coordinates generated by robotic milking systems in North American Holstein cattle

Udder conformation is directly related to milk yield, cow health, workability, and welfare. Automatic milking systems (AMS, also known as milking robots) have become popular worldwide, and the number of dairy farms adopting these systems have increased considerably over the past years. In each milking visit, AMS record the location of the 4 teats as Cartesian coordinates in a xyz plan, which can then be used to derive udder conformation traits. AMS generate a large amount of per milking visit data for individual cows, which contribute to an accurate assessment of important traits such as udder conformation without the addition of human classifier errors (in subjective scoring systems). Therefore, the primary objectives of this study were to estimate genomic-based genetic parameters for udder conformation traits derived from AMS records in North American Holstein cattle and to assess the genetic correlation between the derived traits for evaluating the feasibility of multi-trait genomic selection for breeding cows that are more suitable for milking in AMS. The Cartesian teat coordinates measured during each milking visit were collected by 36 milking robots in 4,480 Holstein cows from 2017 to 2021, resulting in 5,317,488 records. A total of 4,118 of these Holstein cows were also genotyped for 57,600 single nucleotide polymorphisms. Five udder conformation traits were derived: udder balance (UB, mm), udder depth (UD, mm), front teat distance (FTD, mm), rear teat distance (RTD, mm), and distance front-rear (DFR, mm). In addition, 2 traits directly related to cow productivity in the system were added to the study: daily milk yield (DY) and milk electroconductivity (EC; as an indicator of mastitis). Variance components and genetic parameters for UB, UD, FTD, RTD, DFR, DY, and EC were estimated based on repeatability animal models. The estimates of heritability (±standard error, SE) for UB, UD, FTD, RTD, DFR, DY, and EC were 0.41 ± 0.02, 0.79 ± 0.01, 0.53 ± 0.02, 0.40 ± 0.02, 0.65 ± 0.02, 0.20 ± 0.02, and 0.46 ± 0.02, respectively. The repeatability estimates (±SE) for UB, UD, FTD, RTD, and DFR were 0.82 ± 0.01, 0.93 ± 0.01, 0.87 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. The strongest genetic correlations were observed between the FTD and RTD (0.54 ± 0.03), UD and DFR (-0.47 ± 0.03), DFR and FTD (0.32 ± 0.03), and UD and FTD (-0.31 ± 0.03). These results suggest that udder conformation traits derived from Cartesian coordinates from AMS are moderately to highly heritable. Furthermore, the moderate genetic correlations between these traits should be considered when developing selection sub-indexes. The most relevant genetic correlations between traits related to cow milk productivity and udder conformation traits were between UD and EC (-0.25 ± 0.03) and between DFR and DY (0.30 ± 0.04), in which both genetic correlations are favorable. These findings will contribute to the design of genomic selection schemes for improving udder conformation in North American Holstein cattle, especially in precision dairy farms.

Human-animal interactions and machine-animal interactions in animals under human care: A summary of stakeholder and researcher perceptions and future directions

Animals under human care are exposed to a potentially large range of both familiar and unfamiliar humans. Human-animal interactions vary across settings, and individuals, with the nature of the interaction being affected by a suite of different intrinsic and extrinsic factors. These interactions can be described as positive, negative or neutral. Across some industries, there has been a move towards the development of technologies to support or replace human interactions with animals. Whilst this has many benefits, there can also be challenges associated with increased technology use. A day-long Animal Welfare Research Network workshop was hosted at Harper Adams University, UK, with the aim of bringing together stakeholders and researchers (n = 38) from the companion, farm and zoo animal fields, to discuss benefits, challenges and limitations of human-animal interactions and machine-animal interactions for animals under human care and create a list of future research priorities. The workshop consisted of four talks from experts within these areas, followed by break-out room discussions. This work is the outcome of that workshop. The key recommendations are that approaches to advancing the scientific discipline of machine-animal interactions in animals under human care should focus on: (1) interdisciplinary collaboration; (2) development of validated methods; (3) incorporation of an animal-centred perspective; (4) a focus on promotion of positive animal welfare states (not just avoidance of negative states); and (5) an exploration of ways that machines can support a reduction in the exposure of animals to negative human-animal interactions to reduce negative, and increase positive, experiences for animals.

Attitudes of western Canadian dairy farmers toward technology

Dairy farms have become more reliant on technology. The overall aim of this study was to better understand how dairy farmers view technology and its effects on animal care, including their views on the prospect of integrating gene-editing technology in the future. Virtual-semistructured interviews were conducted with dairy farmers (n = 11) from British Columbia and Alberta. To facilitate discussion, the participants were asked to develop and discuss a timeline describing when and why various technologies were adopted on their farm. Although farmers defined technology broadly and affecting multiple aspects of farm management, this paper focuses on their views regarding how technology can affect animal care. Following thematic analysis of the data, the following 3 themes emerged: (1) the changing role of the farmer (including intergenerational considerations and learning new technology), (2) the effect of technology on the cow and her relationship with the farmer and, (3) technology as the future of the farm. The discussions also highlight the concerns that some farmers have regarding challenges associated with reduced human-animal interactions and effective use of the large amounts of data that are collected through technology. We also specifically asked the participants their views about gene editing as a potential future technology. Most of the participants did not specifically address their views on gene editing, but they spoke about the effect on genetic technologies more generally, often making references to genomic testing. However, some questioned how this technology may affect farmers more generally and spoke about how it could affect human-animal relationships. These results illustrate differences among farmers in the way they view technology and how this can affect the dairy cattle they care for.

Unlocking insights: text mining analysis on the health, welfare, and behavior of cows in automated milking systems

Automated Milking Systems (AMS) have undergone significant evolution over the past 30 yr, and their adoption continues to increase, as evidenced by the growing scientific literature. These systems offer advantages such as a reduced milking workload and increased milk yield per cow. However, given concerns about the welfare of farmed animals, studying the effects of AMS on the health and welfare of animals becomes crucial for the overall sustainability of the dairy sector. In the last few years, some analysis conducted through text mining (TM) and topic analysis (TA) approaches have become increasingly widespread in the livestock sector. The aim of the study was to analyze the scientific literature on the impact of AMS on dairy cow health, welfare, and behavior: the paper aimed to produce a comprehensive analysis on this topic using TM and TA approaches. After a preprocessing phase, a dataset of 427 documents was analyzed. The abstracts of the selected papers were analyzed by TM and a TA using Software R 4.3.1. A Term Frequency-Inverse Document Frequency (TFIDF) technique was used to assign a relative weight to each term. According to the results of the TM, the ten most important terms, both words and roots, were feed, farm, teat, concentr, mastiti, group, SCC (somatic cell count), herd, lame and pasture. The 10 most important terms showed TFIDF values greater than 3.5, with feed showing a value of TFIDF of 5.43 and pasture of 3.66. Eight topics were selected with TA, namely: 1) Cow traffic and time budget, 2) Farm management, 3) Udder health, 4) Comparison with conventional milking, 5) Milk production, 6) Analysis of AMS data, 7) Disease detection, 8) Feeding management. Over the years, the focus of documents has shifted from cow traffic, udder health and cow feeding to the analysis of data recorded by the robot to monitor animal conditions and welfare and promptly identify the onset of stress or diseases. The analysis reveals the complex nature of the relationship between AMS and animal welfare, health, and behavior: on one hand, the robot offers interesting opportunities to safeguard animal welfare and health, especially for the possibility of early identification of anomalous conditions using sensors and data; on the other hand, it poses potential risks, which requires further investigations. TM offers an alternative approach to information retrieval in livestock science, especially when dealing with a substantial volume of documents.

Comparison of selected parameters of automated milking in dairy cattle barns equipped with a concentrate feeding system

Automatic milking systems (AMSs) give cows relative freedom to choose the time and frequency of milking throughout the day. Feeding stations also may improve the management of farms. Combining milking robots and feeding stations (FS) may improve milking efficiency and milk yield. Therefore, combining AMS and FS may be beneficial for farmers. The objective of the research was to compare selected automatic milking parameters (daily indices per cow) registered by an AMS in relation to selected features including the presence of concentrate feeding stations. We analysed 931 cows born in 2013-14, in lactations 1-8. In total, we collected data from 357 318 milking days. The following parameters were examined: milking frequency (n/24 h), number of rejected milking (n/24 h), the average number of nipple attempts (n/milking), milking speed (kg/min), time spent in the milking box (s/24 h), milk yield (kg/24 h), milking efficiency (kg/min), rumination time (min/24 h), and concentrate intake (kg) per 100 kg of milk produced. The statistical analysis was conducted using a multi-factor analysis of variance. The analysis confirmed a statistical effect of the concentrate feeding system on most of the investigated traits, except for nipple attempts, box time and rumination time. In cows in barns with an FS, the following parameters were statistically higher compared to cows in non-FS barns: milking frequency (3.04 vs 2.73n/24 h), number of rejected milking (2.24 vs 1.51n/24 h), milking speed (2.98 vs 2.64 kg/min), milk yield (33.48 vs 30.14 kg/24 h), milking efficiency (1.80 vs 1.67 kg/min), and concentrate intake per 100 kg of milk produced (14.67 vs 12.67 kg). The study results indicate that using feeding stations in combination with an AMS can increase milking efficiency, hence the milk output from a milking robot.

Geographical trends for automatic milking systems research in non-pasture-based dairy farms: A scoping review

Automatic milking system (AMS) adoption in the United States is trending upward, with issues such as lower availability and increased cost of labor being factors frequently listed as motives for AMS implementation. In addition, more interest in precision dairy farming by the new generation of farmers may also help increase AMS adoption. The objective of this scoping review was to characterize the nature of the literature investigating non-pasture-based AMS and the opportunities and challenges for future research. The eligibility criteria included studies published in or after the year 2000, with full text in English, of at least 500 words, examining various outcomes related to AMS in non-pasture-based dairy farms. Six electronic databases were searched: Biosis (Web of Science), CAB Abstracts (CAB Direct), Medline (PubMed), PubAg, AGRIS (FAO), and Scopus (Elsevier). The review focused on studies with objectives, characteristics, farms, and AMS information. A total of 4,292 titles and abstracts were screened, and 536 studies were finally included. Most of the studies were conducted in Europe (73.5%), among commercial herds (67.9%), comprising Holstein cows (57.7%), using Lely and DeLaval brands (45.4% vs. 39.7%), with free-flow traffic (52.7%). The main research topics investigated were milk production, milk composition, and AMS efficiency, followed by behavior and welfare, health disorders (especially mastitis), and nutrition in Europe and other regions. At the same time, in the United States, trends were similar, except for nutrition. Since 2016, there has been an increased interest in studies on energy and water consumption, technological development, environment (enteric emissions), reproduction, genetics, and longevity or culling. However, the small number of studies and unclear characterization of what is optimum for reproductive management, other health disorders, economics, and water and energy consumption suggest a need for future research.

Genomic Selection for Dairy Cattle Behaviour Considering Novel Traits in a Changing Technical Production Environment

Cow behaviour is a major factor influencing dairy herd profitability and is an indicator of animal welfare and disease. Behaviour is a complex network of behavioural patterns in response to environmental and social stimuli and human handling. Advances in agricultural technology have led to changes in dairy cow husbandry systems worldwide. Increasing herd sizes, less time availability to take care of the animals and modern technology such as automatic milking systems (AMSs) imply limited human-cow interactions. On the other hand, cow behaviour responses to the technical environment (cow-AMS interactions) simultaneously improve production efficiency and welfare and contribute to simplified "cow handling" and reduced labour time. Automatic milking systems generate objective behaviour traits linked to workability, milkability and health, which can be implemented into genomic selection tools. However, there is insufficient understanding of the genetic mechanisms influencing cow learning and social behaviour, in turn affecting herd management, productivity and welfare. Moreover, physiological and molecular biomarkers such as heart rate, neurotransmitters and hormones might be useful indicators and predictors of cow behaviour. This review gives an overview of published behaviour studies in dairy cows in the context of genetics and genomics and discusses possibilities for breeding approaches to achieve desired behaviour in a technical production environment.

Effects of dairy cows' personality traits on their adaptation to an automated milking system following parturition

The objectives of this study were to determine how dairy cow personality traits affect their adaptation to an automated milking system (AMS) upon parturition, as well as whether these personality traits are consistent across the transition from gestation to lactation. Sixty Holstein dairy cows (19 primiparous and 41 multiparous) were assessed for personality traits using a combined arena test conducted at 24 d before parturition and 24 d after first introduction to an AMS, which occurred ∼3 d after parturition. The combined arena test comprised 3 parts: a novel arena test, a novel object test, and a novel human test. Principal component analysis of the behaviors recorded during the personality assessment revealed 3 factors interpreted as personality traits (75% cumulative variance) in the pre-calving test, interpreted as explore, active, and bold. The post-calving test revealed 2 factors (78% cumulative variance), interpreted as active and explore. Data from d 1 to 7 after introduction to the AMS were summarized by cow and associated with the pre-calving factors, and data from d 21 to 27 after introduction to the AMS were summarized by cow and associated with the post-calving factors. The active trait had a moderate positive correlation between the pre- and post-calving tests, whereas exploration had a weak positive correlation between tests. Cows that scored high for activeness in the pre-calving test tended to have fewer fetching events and a higher coefficient of variation of milk yield in the first 7 d after introduction to the AMS, whereas bolder cows tended to have higher milk yield during that period. In the post-calving test, more active cows tended to have more frequent milkings and voluntary visits per day, as well as a lower cumulative milk yield from d 21 to 27 after introduction to the AMS. Overall, these results indicate that personality traits of dairy cows are associated with adaptation and performance in an AMS, and that personality traits are consistent across the transition period. Specifically, cows that scored high for boldness and activeness adapted better to the AMS immediately after calving, whereas cows that scored low for activeness and high for boldness performed better in terms of milk yield and milking activity in early lactation. This study demonstrates that personality traits affect milking activity and milk yield of dairy cows milked with an AMS and, therefore, may be useful for selection of cows who might best adapt to and use an AMS.

Scientific Productions on Precision Livestock Farming: An Overview of the Evolution and Current State of Research Based on a Bibliometric Analysis

The interest in precision livestock farming (PLF)-a concept discussed for the first time in the early 2000s-has advanced considerably in recent years due to its important role in the development of sustainable livestock production systems. However, a comprehensive bibliometric analysis of the PLF literature is lacking. To address this gap, this study analyzed documents published from 2005 to 2021, aiming to understand the historical influences on technology adoption in livestock farming, identify future global trends, and examine shifts in scientific research on this topic. By using specific search terms in the Web of Science Core Collection, 886 publications were identified and analyzed using the bibliometrix R-package. The analysis revealed that the collection consisted mostly of research articles (74.6%) and reviews (10.4%). The top three core journals were the Journal of Dairy Science, Computers and Electronics in Agriculture, and Animals. Over time, the number of publications has steadily increased, with a higher growth rate in the last five years (29.0%) compared to the initial period (13.7%). Authors and institutions from multiple countries have contributed to the literature, with the USA, the Netherlands, and Italy leading in terms of publication numbers. The analysis also highlighted the growing interest in bovine production systems, emphasizing the importance of behavioral studies in PLF tool development. Automated milking systems were identified as central drivers of innovation in the PLF sector. Emerging themes for the future included "emissions" and "mitigation", indicating a focus on environmental concerns.

A Literature Review of Modeling Approaches Applied to Data Collected in Automatic Milking Systems

Automatic milking systems (AMS) have played a pioneering role in the advancement of Precision Livestock Farming, revolutionizing the dairy farming industry on a global scale. This review specifically targets papers that focus on the use of modeling approaches within the context of AMS. We conducted a thorough review of 60 articles that specifically address the topics of cows' health, production, and behavior/management Machine Learning (ML) emerged as the most widely used method, being present in 63% of the studies, followed by statistical analysis (14%), fuzzy algorithms (9%), deterministic models (7%), and detection algorithms (7%). A significant majority of the reviewed studies (82%) primarily focused on the detection of cows' health, with a specific emphasis on mastitis, while only 11% evaluated milk production. Accurate forecasting of dairy cow milk yield and understanding the deviation between expected and observed milk yields of individual cows can offer significant benefits in dairy cow management. Likewise, the study of cows' behavior and herd management in AMSs is under-explored (7%). Despite the growing utilization of machine learning (ML) techniques in the field of dairy cow management, there remains a lack of a robust methodology for their application. Specifically, we found a substantial disparity in adequately balancing the positive and negative classes within health prediction models.

Water-Energy-Food nexus index proposal as a sustainability criterion on dairy farms

Cow milk is a fundamental nutrients source for the human diet at all stages of life. However, the decline in cow milk consumption over the years has been driven by increased consumer awareness of animal welfare and the environmental burdens associated. In this regard, different initiatives have emerged to mitigate the impacts of livestock farming, but many of them without addressing the multi-perspective view of environmental sustainability. Thus, the Water-Energy-Food (WEF) nexus emerges as a framework to consider the complex synergies among carbon emissions, water demand, energy requirements and food production. In this study, a novel and harmonised WEF nexus approach has been proposed and applied to evaluate a set of 100 dairy farms. For that, the assessment, normalisation, and weighting of three lifecycle indicators such as carbon, water and energy footprints, as well as the milk yield were carried out to obtain a single value, the WEF nexus index (WEFni), which varies from 0 to 100. Results show that the WEF nexus scores obtained vary from 31 to 90, demonstrating large differences among the farms assessed. A cluster ranking was performed to identify those farms with the worst WEF nexus indexes. For this group, consisting of 8 farms with an average WEFni of 39, three improvement actions focused on the feeding, digestive process and wellbeing of the cows were applied to determine the potential reduction in the two main hotspots identified: cow feeding and milk production level. The proposed methodology can establish a roadmap for promoting a more environmentally sustainable food industry, although further studies are still required in the pathway of a standardised WEFni.

Coxiella burnetii Seroprevalence and Associated Risk Factors in Cattle, Sheep, and Goats in Estonia

Q fever, a disease caused by Coxiella burnetii (CB), is an emerging zoonotic health problem. The prevalence data from potential sources are valuable for assessing the risk to human and animal health. To estimate the prevalence of CB antibodies in Estonian ruminants, pooled milk and serum samples from cattle (Bos taurus) and pooled serum samples from sheep (Ovis aries) and goats (Capra hircus) were analyzed. Additionally, bulk tank milk samples (BTM; n = 72) were analyzed for the presence of CB DNA. Questionnaires and herd-level datasets were used to identify the risk factors for exposure using binary logistic regression analysis. The prevalence of CB-positive dairy cattle herds (27.16%) was significantly higher than that in beef cattle herds (6.67%) and sheep flocks (2.35%). No CB antibodies were detected in the goat flocks. CB DNA was found in 11.36% of the BTM samples. The odds of seropositivity were higher in dairy cattle herds, with an increasing number of cattle in the herd, and with location in southwestern, northeastern and northwestern Estonia. Dairy cattle herds had higher odds of testing positive for CB in BTM if the dairy cows were kept loose and lower odds if the herd was located in northwestern Estonia.

Genomic-based genetic parameters for milkability traits derived from automatic milking systems in North American Holstein cattle

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from -0.48 to -0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (-0.58 ± 0.02) and MFAIL and FRM (-0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.

Association between Milk Electrical Conductivity Biomarkers with Lameness in Dairy Cows

Early identification of lameness at all phases of lactation improves milk yield and reduces the incidence of mastitis in the herd. According to the literature we hypothesized that there are associations of electrical conductivity variables of milk flow with lameness in dairy cows. The aim of this study was to determine if blood cortisol and electrical conductivity in the milk flow phases correlate with each other and whether they are related to cow lameness. On one farm, out of 1500 cows, 64 cows with signs of lameness and 56 healthy cows were selected with an average of 2.8 lactations and 60 days in the postpartum period. A local veterinarian who specializes in hoof care treatments identified and scored lameness. During evening milking, the milk flow of all 120 cows was measured using electronic milk flow meters (Lactocorder®, WMB AG, Balgache, Switzerland). Before each milking, two electronic mobile milk flow meters (Lactocorders) were mounted between the milking apparatus and the milking tube to take measurements. We found that the average cortisol concentration in the blood of the studied cows was significantly correlated with the laminitis score. Results of this study indicate that the number of non-lame cows with a milk electrical conductivity level of <6 mS/cm even reached 90.8−92.3% of animals. Milk electrical conductivity indicators ≥ 6 mS/cm were determined in 17.8−29.0% more animals in the group of lame cows compared to the group of non-lame cows. According to our study, we detected that blood cortisol concentration had the strongest positive correlation with milk electrical conductivity indicators. Cows with a greater lameness score had a higher cortisol content and milk conductivity.

Mastitis: Impact of Dry Period, Pathogens, and Immune Responses on Etiopathogenesis of Disease and its Association with Periparturient Diseases

Mastitis is an inflammation of the mammary gland initiated by pathogenic bacteria. In fact, mastitis is the second most important reason for the culling of cows from dairy herds, after infertility. In this review we focus on various forms of mastitis, including subclinical and clinical mastitis. We also stress the importance of the dry-off period as an important time when pathogenic bacteria might start their insult to the mammary gland. An important part of the review is the negative effects of mastitis on milk production and composition, as well as economic consequences for dairy farms. The two most important groups of bacteria that are involved in infection of the udder, Gram-negative and Gram-positive bacteria, are also discussed. Although all cows have both innate and adaptive immunity against most pathogens, some are more susceptible to the disease than others. That is why we summarize the most important components of innate and adaptive immunity so that the reader understands the specific immune responses of the udder to pathogenic bacteria. One of the most important sections of this review is interrelationship of mastitis with other diseases, especially retained placenta, metritis and endometritis, ketosis, and laminitis. Is mastitis the cause or the consequence of this disease? Finally, the review concludes with treatment and preventive approaches to mastitis.

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.

Decision tree analysis to evaluate risks associated with lameness on dairy farms with automated milking systems

Lameness is an endemic disorder causing health problems and production losses in the dairy cow industry. The objective of this study was to identify cow and farm-level factors associated with lameness on Automatic Milking System (AMS) farms, using decision tree analysis to assign probabilities to each input. AMS farms across Canada and Michigan were evaluated to identify the most substantial farm (i.e., stall design, bedding) and cow-level (i.e., BCS, leg injuries) factors associated with prevalence of lameness. To assess lameness, videos of cows were used, and cows with a head bob or noticeable limp were categorized as lame. A decision tree classification model used 1378 data points from 39 pens across 36 farms to predict the value of the target class through “tree function” in MATLAB. The primary classifier was identified as type of stall base, dividing the data set into 3 categories: 1) rubber, sand, or geotextile mat flooring, 2) concrete base, and 3) other types of stall base. Within the first category (class membership (CM) = 976), bedding quantity was the secondary classifier, which was divided by cows standing on ≥2 cm (CM=456) or &lt;2 cm (CM=520) of bedding. Bedding quantity was divided into the third most important classifier of BCS, and cow fit stall width. Cows with BCS of 3.25 to 4.5 (CM=307) were defined as non-lame with an estimated probability (EP) of 0.59, while cows with BCS of 2 to 2.5 (CM=213) were further split by hock lesion incidence. Cows without lesions were defined non-lame (EP=0.93) and cows with lesions were defined lame (EP=0.07). Cows that fit stall width were defined as non-lame (EP=0.66) and cows that did not fit were further divided by the width of the feed alley. Farms with ≥430 cm feed alley were defined as non-lame (EP=0.89), whereas farms with &lt;430 cm feed alley were defined as lame (EP=0.11). Through implementing a novel multifactorial approach of data analysis, we were able to highlight the critical points that can be focused on to enhance farm-level housing and management practices or mitigate or monitor cow-level issues to reduce incidence and severity of lameness in AMS farms.

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

In automatic milking systems (AMSs), the detection of clinical mastitis (CM) and the subsequent separation of abnormal milk should be reliably performed by commercial AMSs. Therefore, the objectives of this cross-sectional study were (1) to determine the sensitivity (SN) and specificity (SP) of CM detection of AMS by the four most common manufacturers in Bavarian dairy farms, and (2) to identify routinely collected cow data (AMS and monthly test day data of the regional Dairy Herd Improvement Association (DHIA)) that could improve the SN and SP of clinical mastitis detection. Bavarian dairy farms with AMS from the manufacturers DeLaval, GEA Farm Technologies, Lely, and Lemmer-Fullwood were recruited with the aim of sampling at least 40 cows with clinical mastitis per AMS manufacturer in addition to clinically healthy ones. During a single farm visit, cow-level milking information was first electronically extracted from each AMS and then all lactating cows examined for their udder health status in the barn. Clinical mastitis was defined as at least the presence of visibly abnormal milk. In addition, available DHIA test results from the previous six months were collected. None of the manufacturers provided a definition for clinical mastitis (i.e., visually abnormal milk), therefore, the SN and SP of AMS warning lists for udder health were assessed for each manufacturer individually, based on the clinical evaluation results. Generalized linear mixed models (GLMMs) with herd as random effect were used to determine the potential influence of routinely recorded parameters on SN and SP. A total of 7411 cows on 114 farms were assessed; of these, 7096 cows could be matched to AMS data and were included in the analysis. The prevalence of clinical mastitis was 3.4% (239 cows). When considering the 95% confidence interval (95% CI), all but one manufacturer achieved the minimum SN limit of >80%: DeLaval (SN: 61.4% (95% CI: 49.0%−72.8%)), GEA (75.9% (62.4%−86.5%)), Lely (78.2% (67.4%−86.8%)), and Lemmer-Fullwood (67.6% (50.2%−82.0%)). However, none of the evaluated AMSs achieved the minimum SP limit of 99%: DeLaval (SP: 89.3% (95% CI: 87.7%−90.7%)), GEA (79.2% (77.1%−81.2%)), Lely (86.2% (84.6%−87.7%)), and Lemmer-Fullwood (92.2% (90.8%−93.5%)). All AMS manufacturers’ robots showed an association of SP with cow classification based on somatic cell count (SCC) measurement from the last two DHIA test results: cows that were above the threshold of 100,000 cells/mL for subclinical mastitis on both test days had lower chances of being classified as healthy by the AMS compared to cows that were below the threshold. In conclusion, the detection of clinical mastitis cases was satisfactory across AMS manufacturers. However, the low SP will lead to unnecessarily discarded milk and increased workload to assess potentially false-positive mastitis cases. Based on the results of our study, farmers must evaluate all available data (test day data, AMS data, and daily assessment of their cows in the barn) to make decisions about individual cows and to ultimately ensure animal welfare, food quality, and the economic viability of their farm.

Assessment of technical-productive aspects in Italian dairy farms equipped with automatic milking systems: A multivariate statistical analysis approach

The aim of this study was to assess technical-productive aspects of dairy farms equipped with automatic milking system (AMS) in Northern and Central Italy. A survey was carried out on 62 dairy farms selected through convenience sampling with the following inclusion criteria: adoption of robotic milking for at least 1 yr and ability to provide farm data. Data were collected using a structured questionnaire to obtain a general description of farm characteristics and overall management practices. Through the combination of principal component analysis and k-means cluster analysis, the farms were allocated in 3 clusters. The identified clusters were described and afterward compared using one-way ANOVA or a chi-squared test. The main observed differences between clusters were the average number of lactating cows and AMS installed, average annual milk production, average AMS loading, average annual milk yield per full-time employee, average daily milk yield per cow and AMS, and the average annual veterinary costs per cow. cluster 1 (n = 24) included small-to-medium-sized semi-intensive farms with low AMS loading and low average daily milk yield per cow. In this farm typology, the AMS is not fully used and is likely perceived as a means to improve quality of life rather than profitability. Clusters 2 (n = 31) and 3 (n = 7) included, respectively, small-medium-sized and large intensive farms. These 2 farm typologies are characterized by an intensive approach to dairy cattle breeding, with average higher AMS loading, labor efficiency, and milk yield compared with the farms of cluster 1, likely due to better farm management. This classification could help dairy technicians give farmers customized management advice for the function of the cluster they belong to, and farmers falling in a specific cluster could evaluate whether they are reaching their objectives.

Effects of Housing and Management Factors on Selected Indicators of the Welfare Quality® Protocol in Loose-Housed Dairy Cows

The objective of this study was to examine the effects of housing and management factors on animal welfare indicators in dairy cows using a benchmarking approach. In total, 63 conventional dairy cattle farms with zero-grazing in Northern Germany were assessed using selected animal welfare indicators (body condition score, integument alterations, lameness, milk somatic cell count, and social behaviour) of the Welfare Quality® protocol. Additionally, housing characteristics such as designs of barns, cubicles, and floors were documented during farm visits and farmers were interviewed concerning their common management routines. Farms were categorized into a high welfare or low welfare group by calculating upper and lower tertiles for each of the animal welfare indicators separately. Both groups were compared regarding housing conditions and management practices using univariable and multivariable logistic regressions. Several associations between housing and management factors and animal welfare indicators were demonstrated in univariable analysis (p < 0.20). Significant effects within multivariable logistic regression analysis were determined for lameness (routine use of foot-baths), milk somatic cell count (milking frequency) and social behaviour (cow-to-stall ratio) (p < 0.05). Comparing farms with higher and lower animal welfare status can provide useful information about effective options to improve animal welfare.

Field and Remote Sensors for Environmental Health and Food Safety Diagnostics: An Open Challenge

Major foodborne disease outbreaks have clarified the close interconnection and interdependence between the health of humans, animals, and the environment [...]

Detecting Ultra- and Circadian Activity Rhythms of Dairy Cows in Automatic Milking Systems Using the Degree of Functional Coupling—A Pilot Study

Ultra- and circadian activity rhythms of animals can provide important insights into animal welfare. The consistency of behavioral patterns is characteristic of healthy organisms, while changes in the regularity of behavioral rhythms may indicate health and stress-related challenges. This pilot study aimed to examine whether dairy cows in free-stall barns with an automatic milking system (AMS) and free cow traffic can develop ultra- and circadian activity rhythms. On 4 dairy farms, pedometers recorded the activity of 10 cows each over 28 days. Based on time series calculation, the Degree of Functional Coupling (DFC) was used to determine the cows' activity rhythms. The DFC identified significant rhythmic patterns in sliding 7-day periods and indicated the percentage of activity (0–100%) that was synchronized with the 24-h day-night rhythm. As light is the main factor influencing the sleep-wake cycle of organisms, light intensity was recorded in the AMS, at the feed alley and in the barn of each farm. In addition, feeding and milking management were considered as part of the environmental context. Saliva samples of each cow were taken every 3 h for 1 day to determine the melatonin concentration. The DFC approach was successfully used to detect activity rhythms of dairy cows in commercial housing systems. However, large inter- and intra-individual variations were observed. Due to a high frequency of 0 and 100%, a median split was used to dichotomize into “low” (&lt;72.34%) and “high” (≥72.34%) DFC. Forty percent of the sliding 7-day periods corresponded to a low DFC and 50% to a high DFC. No DFC could be calculated for 10% of the periods, as the cows' activity was not synchronized to 24 h. A generalized linear mixed-effects model revealed that the DFC levels were positively associated with a longer milking interval and a higher amount of daytime activity and negatively associated with higher number of lactations. The DFC is a novel approach to animal behavior monitoring. Due to its automation capability, it represents a promising tool in its further development for the purpose of longitudinal monitoring of animal welfare.

Comparison of raw cow milk microbiota in two milking systems: a field study

This study investigated the effect of different milking systems on the milk composition and microbial community of raw milk in a commercial dairy farm. Raw milk samples from conventional milking system (CMS) and automatic milking system (AMS) were collected and the microbiota on each was assessed by 16S rRNA gene sequencing. Results showed that the acetone (P = 0.031) and β-hydroxybutyrate (P≤0.001) levels in the raw milk of the AMS group were increased compared with the CMS group. Principal component analysis, unweighted and weighted principal coordinates analysis, and heat map of microbial community composition in the raw milk showed a clear separation between two groups. AMS increased the abundance of the genera Acinetobacter (FDR = 0.004) and Staphylococcus (FDR = 0.004) in the raw milk compared with the CMS group. In contrast, the abundance of the genera Pseudomonas (FDR = 0.028), Lactococcus (FDR = 0.015), Sphingobacterium (FDR = 0.004), Brevundimonas (FDR = 0.005), and Chryseobacterium (FDR = 0.042) in the raw milk was reduced in the AMS group compared with the CMS group. The abundance of the genera Acinetobacter and Staphylococcus in the raw milk was positively correlated with the β-hydroxybutyrate, acetone, free fatty acid, citric acid, and urea nitrogen levels. Furthermore, the abundance of the genus Acinetobacter in the raw milk was negatively correlated with the somatic cell counts. The study demonstrates that the introduction of AMS in the dairy farm can regulate microbiota composition in the raw milk and this modification may exert an effect on reducing the somatic cell counts in the raw milk.

The prevalence of Campylobacter spp., Listeria monocytogenes and Shiga toxin-producing Escherichia coli in Norwegian dairy cattle farms; a comparison between free stall and tie stall housing systems

Aims

This study explored how dairy farm operating systems with free‐stall or tie‐stall housing and cow hygiene score influence the occurrence of zoonotic bacteria in raw milk.

Methods and Results

Samples from bulk tank milk (BTM), milk filters, faeces, feed, teats and teat milk were collected from 11 farms with loose housing and seven farms with tie‐stall housing every second month over a period of 11 months and analysed for the presence of STEC by culturing combined with polymerase chain reaction and for Campylobacter spp. and L. monocytogenes by culturing only. Campylobacter spp., L. monocytogenes and STEC were present in samples from the farm environment and were also detected in 4%, 13% and 7% of the milk filters, respectively, and in 3%, 0% and 1% of BTM samples. Four STEC isolates carried the eae gene, which is linked to the capacity to cause severe human disease. L. monocytogenes were detected more frequently in loose housing herds compared with tie‐stalled herds in faeces (p = 0.02) and feed (p = 0.03), and Campylobacter spp. were detected more frequently in loose housing herds in faeces (p < 0.01) and teat swabs (p = 0.03). An association between cow hygiene score and detection of Campylobacter spp. in teat milk was observed (p = 0.03).

Conclusion

Since some samples collected from loose housing systems revealed a significantly higher (p < 0.05) content of L. monocytogenes and Campylobacter spp. than samples collected from tie‐stalled herds, the current study suggests that the type of housing system may influence the food safety of raw milk.

Significance and Impact of the Study

This study highlights that zoonotic bacteria can be present in raw milk independent of hygienic conditions at the farm and what housing system is used. Altogether, this study provides important knowledge for evaluating the risk of drinking unpasteurized milk.

Associations between Dairy Herds' Qualitative Behavior and Aspects of Herd Health, Stockperson and Farm Factors-A Cross-Sectional Exploration

The affective state is an integrated aspect of farm animal welfare, which is understood as the animals' perception of their living environment and of their internal biological functioning. The aim of this cross-sectional study was to explore animal-internal and external factors potentially influencing dairy cows' affective state. For this purpose, qualitative behavior assessments (QBA) describing the animals' body language were applied at herd level on 25 dairy farms. By means of principal component analysis (PCA), scores of PC1 (QBAscores) were determined for further analyses. From monthly milk recordings (MR) one year retrospectively, prevalences of udder and metabolic health impairments were calculated. Factors of housing, management, and human-animal contact were recorded via interviews and observations. A multivariable regression was calculated following a univariable preselection of factors. No associations were found between MR indicators and QBAscores. However, more positive QBAscores were associated with bedded cubicles or straw yards compared to raised cubicles, increased voluntary stockperson contact with the cows, and fixation of cows during main feeding times, the latter contributing to the explanatory model, but not being significant. These results underline the importance of lying comfort, positive human-animal relationship and reduction of competition during feeding for the well-being of dairy cows.

The Effect of Music on Livestock: Cattle, Poultry and Pigs

Simple Summary

In times of intensified livestock production, the search for methods that reduce stress, which has an adverse impact on the health and welfare of their animals, has become a challenge for breeders and producers. Therefore, the possibility of using various musical genres to alleviate stress in chickens, cattle or pigs was considered. It has turned out that choosing a musical item is extremely important, as it can positively affect the health and production performance of animals by increasing the feeling of relaxation. The time of exposure to sounds and their intensity are important as well, and some authors propose to also pay attention to the frequency of sound waves. Music therapy, which was previously more widely deployed among humans, is increasingly used for farm animals as an element of enriching their living environment. Current research shows the importance of sound waves’ influence in animal production. Proper selection of the music genre, music intensity and tempo can reduce the adverse effects of noise and, thus, reduce the level of stress. It should be remembered, however, that silence is equally important and necessary for the welfare of animals. The paper presents literature findings regarding the influence of music on cattle, poultry and pigs.

Abstract

The welfare of animals, especially those kept in intensive production systems, is a priority for modern agriculture. This stems from the desire to keep animals healthy, to obtain a good-quality final product, and to meet the demands of today’s consumers, who have been increasingly persuaded to buy organic products. As a result, new sound-based methods have been pursued to reduce external stress in livestock. Music therapy has been known for thousands of years, and sounds were believed to improve both body and spirit. Today, they are mostly used to distract patients from their pain, as well as to treat depression and cardiovascular disorders. However, recent studies have suggested that appropriately selected music can confer some health benefits, e.g., by increasing the level and activity of natural killer cells. For use in livestock, the choice of genre, the loudness of the music and the tempo are all important factors. Some music tracks promote relaxation (thus improving yields), while others have the opposite effect. However, there is no doubt that enriching the animals’ environment with music improves their welfare and may also convince consumers to buy products from intensively farmed animals. The present paper explores the effects of music on livestock (cattle, poultry and pigs) on the basis of the available literature.

Association between Udder and Quarter Level Indicators and Milk Somatic Cell Count in Automatic Milking Systems

Simple Summary

In dairy cattle herds milked by automatic systems, the absence of a human milker originates the need for control systems to monitor the milking process and cow conditions. Modern milking robots are equipped with a lot of sensors that, at each milking (2.5–3 times a day), record data on milk yield and quality, milking efficiency, cow welfare, and health with particular focus to udder conditions. Mastitis is one of the most frequent and serious diseases of dairy cow that negatively affects milk quality and yield, reduces animal welfare, and often implies the use of antimicrobial drugs. At the moment, the alerting systems for mastitis risk is generally based on monitoring milk electrical conductivity, color, and/or temperature, but these indicators have limited reliability. Other information gathered by automatic sensors, already implemented in commercial robots, could be useful to early detect mastitis. Using a multivariate approach, our study showed that the deviations over time of milk electrical conductivity, milk yield, and milk flow of single quarters in comparison with the whole udder are potential indicators, alone or in combination, for altered udder conditions. The results could be useful for the development of new algorithms more effective in the early detection of mastitis.

Abstract

Automatic Milking Systems (AMS) record a lot of information, at udder and quarter level, which can be useful for improving the early detection of altered udder health conditions. A total of 752,000 records from 1003 lactating cows milked with two types of AMS in four farms were processed with the aim of identifying new indicators, starting from the variables provided by the AMS, useful to predict the risk of high milk somatic cell count (SCC). Considering the temporal pattern, the quarter vs. udder percentage difference in milk electrical conductivity showed an increase in the fourteen days preceding an official milk control higher than 300,000 SCC/mL. Similarly, deviations over time in quarter vs. udder milk yield, average milk flow, and milking time emerged as potential indicators for high SCC. The Logistic Analysis showed that Milk Production Rate (kg/h) and the within-cow within-milking percentage variations of single quarter vs. udder milk electrical conductivity, milk yield, and average milk flow are all risk factors for high milk SCC. The result suggests that these variables, alone or in combination, and their progression over time could be used to improve the early prediction of risk situations for udder health in AMS milked herds.

Implementation of Technical and Technological Progress in Dairy Production

The involvement of people and technical devices is a characteristic feature of technological processes in agriculture. Human access to modernized and more efficient technical equipment determines the differentiation of the proportions of the contributions of human labor and technical equipment to the implementation of production technology on farms. Taking into account the data on manual and machine work inputs, the methodology of determining the technological index level (TL) was presented. The aim of the present study was to present the scope of use of the technological index level to assess the effects of technological progress in the dairy production system, with particular emphasis on cow milking. For the value range of the technological index level (0–100%), changes in the milkman’s work efficiency were presented based on research carried out on farms equipped with milking equipment at different levels of technical advancement. Moreover, the course of changes in electricity and water consumption per liter of milk was determined in association with the technological index level. The issue of simultaneous implementation of various forms of progress was developed based on the example of milking cows with a milking robot. Five categories (ranges) of cows’ milk yield were distinguished and compared with the current yields of cows in the European Union. On this basis, a discussion was initiated on the factors that facilitate and limit the implementation of technical and technological progress in dairy production.

Biometric Physiological Responses from Dairy Cows Measured by Visible Remote Sensing Are Good Predictors of Milk Productivity and Quality through Artificial Intelligence

New and emerging technologies, especially those based on non-invasive video and thermal infrared cameras, can be readily tested on robotic milking facilities. In this research, implemented non-invasive computer vision methods to estimate cow's heart rate, respiration rate, and abrupt movements captured using RGB cameras and machine learning modelling to predict eye temperature, milk production and quality are presented. RGB and infrared thermal videos (IRTV) were acquired from cows using a robotic milking facility. Results from 102 different cows with replicates (n = 150) showed that an artificial neural network (ANN) model using only inputs from RGB cameras presented high accuracy (R = 0.96) in predicting eye temperature (°C), using IRTV as ground truth, daily milk productivity (kg-milk-day^-1), cow milk productivity (kg-milk-cow^-1), milk fat (%) and milk protein (%) with no signs of overfitting. The ANN model developed was deployed using an independent 132 cow samples obtained on different days, which also rendered high accuracy and was similar to the model development (R = 0.93). This model can be easily applied using affordable RGB camera systems to obtain all the proposed targets, including eye temperature, which can also be used to model animal welfare and biotic/abiotic stress. Furthermore, these models can be readily deployed in conventional dairy farms.

Farm-level factors associated with lameness prevalence, productivity, and milk quality in farms with automated milking systems

Impaired locomotion (lameness) may negatively affect the ability and desire of cows to milk voluntarily, which is a key factor in success of automated milking systems (AMS). The objective of this study was to identify factors associated with herd-level lameness prevalence and associations of lameness and other farm-level factors with milking activity, milk yield, and milk quality in herds with AMS. From April to September 2019, 75 herds with AMS in Ontario, Canada, were visited, and data on barn design and farm management practices were collected. Data from AMS were collected, along with milk recording data, for the 6-mo period before farm visits. Farms averaged 98 ± 71 lactating cows, 2.3 ± 1.5 robot units/farm, 43.6 ± 9.4 cows/robot, 36.4 ± 4.9 kg/d of milk, a milking frequency of 3.01 ± 0.33 milkings/d, and a herd average geometric mean SCC of 179.3 ± 74.6 (× 1,000) cells/mL. Thirty percent of cows/farm (minimum of 30 cows/farm) were scored for body condition (1 = underconditioned to 5 = over conditioned) and locomotion (1 = sound to 5 = lame; clinically lame ≥3 out of 5 = 28.3 ± 11.7%, and severely lame ≥4 out of 5 = 3.0 ± 3.2%). Clinical lameness (locomotion score ≥3) was less prevalent on farms with sand bedding, with increased feed bunk space per cow, and on farms with non-Holstein breeds versus Holsteins, and tended to be less prevalent with lesser proportion of underconditioned cows (with body condition score ≤2.5). Severe lameness occurrence (farms with any cows with locomotion score ≥4) was associated with a greater proportion of underconditioned cows and in farms with stalls with greater curb heights. Herd average milk yield/cow per day increased with lesser prevalence of clinical lameness (each 10-percentage-point decrease in clinical lameness prevalence was associated with 2.0 kg/cow per day greater milk yield) and greater milking visit frequency per day, and tended to be greater with increased feed push-up frequency. Lesser herd average somatic cell count was associated with lesser clinical lameness prevalence, herd average days in milk, and proportion of overconditioned cows, and somatic cell count tended to be lesser for farms with sand bedding versus those with organic bedding substrates. The results highlight the importance of minimizing lameness prevalence, using of sand bedding, ensuring adequate feed access and feed bunk space, and maintaining proper cow body condition to optimize herd-level productivity and milk quality in AMS herds.

Comparing steam-flaked and pelleted barley grain in a feed-first guided-flow automated milking system for Holstein cows

Provision of a palatable feed in automated milking systems (AMS) is considered an essential motivating factor to encourage voluntary visits to the milking stall. Although the quantity and composition of AMS concentrates have been previously investigated, the form of the concentrate has not been extensively evaluated. The objective of this study was to evaluate the effects of feeding pelleted (PB; 132.9 ± 56 DIM, 47.4 ± 9.51 kg/d milk yield) versus steam-flaked barley (SFB; 133.0 ± 63 DIM, 40.5 ± 8.23 kg/d milk yield) in an AMS on dry matter intake, AMS visits, milk and milk component yield, and partial mixed ration (PMR) feeding behavior. Twenty-nine Holstein cows of varying parities were enrolled in this study. Cows were housed in freestall housing with a feed-first guided-flow barn design; 7 cows were housed in a separate freestall pen to enable individual PMR intake and feeding behavior monitoring. This study was conducted as a 2-way crossover, with two 21-d periods in which each cow received the same basal PMR but was offered 2 kg/d (dry matter basis) of PB or SFB in the AMS. Cows receiving the SFB had fewer voluntary AMS visits (2.71 vs. 2.90 ± 0.051, no./d), tended to have a longer interval between milkings (541.7 vs. 505.8 ± 21.02 min), spent more time in the holding pen before entering the AMS (139.9 vs. 81.2 ± 11.68 min/d), and had lower total box time (19.7 vs. 21.4 ± 0.35 min/d) than cows fed PB. Despite changes in AMS attendance, there were no differences for average milk (44.0 kg/d), fat (1.62 kg/d), and protein (1.47 kg/d) yields or AMS concentrate intake (2.02 kg/d). These behavioral changes indicate that offering SFB as an alternative to PB may reduce motivation for cows to voluntarily enter the AMS.

Simulation Tests of a Cow Milking Machine—Analysis of Design Parameters

The objective of this paper was to create a mathematical model of vacuum drops in a form that enables the testing of the impact of design parameters of a milking cluster on the values of vacuum drops in the claw. Simulation tests of the milking cluster were conducted, with the use of a simplified model of vacuum drops in the form of a fourth-degree polynomial. Sensitivity analysis and a simulation of a model with a simplified structure of vacuum drops in the claw were carried out. As a result, the impact of the milking machine’s design parameters on the milking process could be analysed. The results showed that a change in the local loss and linear drag coefficient in the long milk duct will have a lower impact on vacuum drops if a smaller flux of inlet air, a higher head of the air/liquid mix, and a higher diameter of the long milk tube are used.

The Topic of the Ideal Dairy Farm Can Inspire How to Assess Knowledge about Dairy Production Processes: A Case Study with Students and Their Contributions

The dairy farm and on-farm production processes are the subject of numerous evaluations. These are not only evaluations of the economic efficiency of milk production. Opinions expressed by various social groups are also an important contribution to improving the approach to milk production on the farm. As a result of such opinions, a vision of an ideal dairy farm may be formed. The aim of the study was to develop the thematic area of an ideal dairy farm in the opinion of two groups of students who were learning remotely (in the distance learning system) due to the Covid-19 pandemic. The first group consisted of six Erasmus+ students. The second group consisted of 70 full-time Polish students. As part of their homework, the students answered three questions about the ideal dairy farm. Students had 4 weeks to do their homework. Erasmus+ students’ homework was used to propose a ranking method for assessing the answers to three questions by the students themselves. Homework of Polish students was used to analyze the frequency of using certain keywords. Polish students, in their homework on the ideal dairy farm, mainly used the basic concepts related to dairy production. Unfortunately, a very small number of students used terms that represent a responsible approach to dairy production, including ethical aspects, freedom, sustainability, animal pain, antibiotics, and organic milk production. In conclusion, it was indicated that the curriculum should be structured in such a way as to raise students’ awareness of dairy production and its current challenges.

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.

Dairy farm-workers' knowledge of factors responsible for culling and mortality in the Eastern Cape Province, South Africa

Milk serves as a significant source of protein for many families and aids in combating food insecurity. However, the demand for milk and milk-related products far exceeds the supply. The objective of the study was to evaluate dairy farm-workers' knowledge of factors responsible for culling and mortality of dairy cows in the Eastern Cape Province. Data was collected from 106 dairy farm-workers using a questionnaire. Any correctly answered question by the majority amounted to a point and a zero for incorrectly answered questions. Correct answering by the majority to more than half the questions of a subsection amounted to a pass. A less than 50% pass rate was considered a poor level of knowledge, 51-69% pass rate was considered an average level of knowledge, and anything higher than that was considered a good level of knowledge. Most farm-workers (66.0%) relied on their colleagues for dairy health information. Most dairy farm-workers (49.1%) indicated that lameness, milk fever (56.6%), and mastitis (47.2%) do not lead to culling and mortality of dairy cows. A majority (83%) of farm-workers agreed that reproduction problems, poor milk yield (77.3%), and age (81.1%) are the main reasons for culling dairy cows. The participants had varying perceptions and limited knowledge (28.3%) about the major contributing factors of culling and mortality. The lack of training courses and minimal use of other sources of information such as the internet might contribute to this poor knowledge and perceptions.

Performance perception of Canadian dairy producers when transitioning to an automatic milking system

Adoption of automated milking systems (AMS) has increased exponentially around the world in recent years. The objective of this observational study was to evaluate how producer perception of changes in cow-average milk yield and somatic cell count (SCC) compared with the actual changes in their herds after the introduction of AMS in Canadian commercial dairy herds. Data were collected (in 2014 and 2015) through a survey of 97 Canadian dairy herds that shifted to AMS from 2000 to 2014. Producers were asked their perception about milk yield and SCC changes (increase, decrease, or no change) after AMS introduction. Actual herd performance data were obtained from dairy herd improvement organizations. Differences between the 12-mo rolling herd-average milk yield (kg/cow per year) and SCC (cells/mL) at the closest test 2 yr after transitioning to AMS and at the last test before the transition were calculated and compared with the producer perception answers. After AMS adoption, milking herd size, milk yield, SCC, and number of AMS units per herd averaged (± standard deviation) 99.8 ± 54.4 cows, 9,619 ± 1,354 kg/cow per year, 248,825 ± 97,286 cells/mL, and 1.9 ± 1.1 units, respectively. On average, after AMS introduction, herd size, milk yield, and culling rate increased by 11.3 cows, 441 kg/cow per year, and 1.3%, respectively, and calving interval decreased by 7 d. For producers who perceived an increase, actual milk yield and SCC increases averaged (mean ± standard deviation) +534 ± 1,003 kg/cow per year and +56,679 ± 66,662 cells/mL, respectively. Alternatively, for producers who perceived a decrease, actual milk yield and SCC decreases averaged -984 ± 658 kg/cow per year and -26,976 ± 94,099 cells/mL, respectively. An actual milk yield change of +83.1 ± 1,113.3 kg/cow per year and an SCC change of +6,135 ± 72,609 cells/mL were observed in the herds in which the dairy producers perceived no change with the AMS introduction. Hence, dairy producers were, on average, able to discern their actual milk yield and SCC changes after AMS adoption. However, the proportions of dairy producers who accurately perceived their actual milk yield and SCC changes after AMS introduction were 39.4% for milk yield (increase: 36.3%; decrease: 100.0%; and no change: 45.5%) and 46.7% for SCC (increase: 50.0%; decrease: 39.0%; and no change: 54.1%). From these results, we concluded that several dairy producers distorted their actual milk yield and SCC changes or were not fully aware of those changes.

Survival of Polish Holstein-Friesian Cows to Second, Third and Fourth Lactation in Conventional and Automatic Milking Systems

The main objective of the study was to determine the effect of transition from a conventional milking system (CMS) to an automatic milking system (AMS) on survival of 6361 Polish Holstein-Friesian cows to second (SL2), third (SL3) and fourth (SL4) lactation as well culling reasons. The cows were born between 2002 and 2015 and calved between 2004 and 2018. All data for the survival analysis and culling reasons of cows in 17 herds during operation of CMS and AMS were extracted from the SYMLEK official milk recording system. Cow survival (SL2, SL3 and SL4) was analysed with multiple logistic regression using the following effects in the model: milking system (MS), first calving season (CS), age at first calving (AFC), ease of first calving (CE), birth of a dead calf at first calving (DC), milk yield (MY) for full first lactation (MY – this effect was ignored in SL2 analysis), herd (H), and MS × H interaction. In the next stage of the study, χ2 test was used to analyse culling reasons of cows (udder diseases, low fertility – infertility and reproductive disorders, locomotor diseases, low milk yield, other diseases – metabolic, digestive and respiratory diseases, accidents and chance events) in the first, second and third lactation and collectively in the first three lactations. Logistic regression analysis indicated a significant effect of MS, AFC, DC on SL2 and SL3, and of MY on SL3 and SL4. Moreover, H and MS × H interaction had a highly significant effect on SL2, SL3, and SL4. Cows used in AMS barns were characterized by significantly worse SL2 and SL3 compared to CMS (odds ratio), by 27.8% and 31.0%, respectively. It was also observed that the effect of switching from CMS to AMS on cow survival was determined by herd membership – in most herds this effect was unfavourable. A distinctly positive effect of milking automation on cow survival (SL2, SL3, SL4) was noted in only one barn (herd) – it was a new barn with a considerably expanded number of milked cows, where the lying area was covered with straw. When analysing the reasons for culling in the first three lactations collectively, it was found that after the AMS system was introduced into the herds, there were increases in the rate of culling for locomotor diseases (by 0.85 percentage points (p.p.)), low milk yield (1.36 p.p.) and other diseases (3.01 p.p.). It was also observed that the automation of milking reduced culling due to udder diseases by 0.37 p.p., low fertility by 3.24 p.p., and accidents and chance events by 1.60 p.p.

Effects of photoperiod and light intensity on milk production and milk composition of dairy cows in automatic milking system

The purpose of this study was to determine the effects of photoperiod and light intensity on milk production, milk composition, hormones levels and blood metabolites indices of Korean Holstein dairy cows in automatic milking system (AMS). A total of 24 Holstein dairy cows were selected and used to four subsequent treatments for the experimental periods of 60 days. The light programs consisted of (1) Control: the natural photoperiod with 14.2 h of the light period and 9.4 h of the dark period (below 10 Lux); (2) T1: 16 h of the long day photoperiod (LDPP) with 50 Lux of light; (3) T2: 16 h of LDPP with 100 Lux of light; and (4) T3: 16 h of LDPP with 200 Lux of light, respectively. Importantly, there was a significant difference in the thurl activity of dairy cows between the different light intensity programs (p < 0.05). Milk yield was higher in T1 and T2 (40.80 ± 1.71 and 39.90 ± 2.02 kg/d, respectively) than those of Control and T3 (32.18 ± 1.51 and 35.76 ± 2.80 kg/d, respectively) (p < 0.05), but DMI was lower in T1, T2, and T3 compared to Control (p < 0.05). Also, milk fat percentage, the contents of milk fat and total solids were higher in T2 than those in the others (p < 0.05). The average daily melatonin level in milk was high to T3 (28.20 ± 0.43 pg/mL), T2 (24.62 ± 0.32 pg/mL), T1 (19.78 ± 0.35 pg/mL), and Control (19.36 ± 0.45 pg/mL) in order (p < 0.05). Also, the cortisol levels in milk and blood were lower in treatment groups than in Control (p < 0.05). The results of this study showed that it will be effective to improve the milk yield and milk composition, and to reduce the stress of dairy cows when the light conditions regulate to extend the photoperiod to 16 h at a light emitting diode (LED) intensity of 100 Lux under the AMS in dairy farm.

Milk production process, quality and technological properties of milk for the use of various types of milking machines

Studies have been carried out to evaluate the efficiency of using easy-assembled cow houses in modern milk production technologies for the use of high-productive milking plants of the type ‘Parallel’ and ‘Carousel’ with 32 machines each. It has been established that new types of premises not only provide comfortable conditions for the maintenance of highly productive cows, but also reduce the labor costs for their maintenance and, most importantly, allow the use of modern high-productive milking installations of the type ‘Parallel’ and ‘Carousel’. It has been established that the technology of preparation of cows for milking and milking technology provides more complete display of the milk ejection reflex at the installation of the ‘Parallel’ type. The average intensity of cows’ milk flows at this plant is 30% higher compared to those of the installation of ‘Carousel’ type, which is confirmed by the intensity of milk flow production at the first minute of milking, which is at the level of 2.97 against 1.85 kg min.-1 per installation of ‘Carousel’ type. Milk obtained using a milking installation of the ‘Parallel’ type has higher values of the mass fraction of fat and protein that is associated with the genetic potential of animals. According to physical, chemical and technological properties, milk obtained from milking installations such as ‘Parallel’ and ‘Carousel’ is within the limits of the standards in force. Milk obtained from the use of milking equipment such as ‘Carousel’ has higher electrical conductivity at the level of 4.6 mS cm-3, which is confirmed by a higher level of mastitis disease of cows. Due to bacterial contamination, reductase test and milk clot characteristic, milk obtained using a milk installation of ‘Parallel’ type also has higher quality indicators than the installation of ‘Carousel’ type. But according to the complex of indicators, milk obtained from various technologies of milking refers to the desired cheese-making class.

A Preliminary Investigation of Social Network Analysis Applied to Dairy Cow Behavior in Automatic Milking System Environments

Simple Summary

Cows are social animals, therefore understanding the ways that they interact can help improve their management and welfare. We used social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). Results from this exploratory study showed that when cows were separated from their affinity-pair cow the day-day variability in milk production increased by a factor of 3, a possible indicator of stress. The results of this exploratory study suggest that SNA could be used as a tool to better understand the social dynamics of dairy cows and inform group and regrouping process to produce positive outcomes.

Abstract

We have applied social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). The SNA was applied to social groups defined by four pens on a dairy farm, each served by an automatic milking system (AMS). Each pen was equipped with an automatic sorting gate that identified when cows voluntarily moved from the resting area to either milking or feeding areas. The aim of this study was two-fold: to determine if SNA could identify affinity pairs and to determine if milk production was affected when affinity pairs where broken. Cow traffic and milking performance data from a commercial guided-flow AMS dairy farm were used. Average number of milked cows was 214 ± 34, distributed in four AMS over 1 year. The SNA was able to identify clear affinity pairs and showed when these pairings were formed and broken as cows entered and left the social group (pen). The trend in all four pens was toward higher-than-expected milk production during periods of affinity. Moreover, we found that when affinities were broken (separation of cow pairs) the day-to-day variability in milk production was three times higher than for cows in an affinity pair. The results of this exploratory study suggest that SNA could be potentially used as a tool to reduce milk yield variation and better understand the social dynamics of dairy cows supporting management and welfare decisions.