Automatic Milking Research Articles

The effects of dietary iodine content, milking system, and farming practices on milk iodine concentration and quality traits

Various management practices can influence milk quality traits in dairy cattle. As an example, an increasing investment in automatic milking system to substitute milking parlors has been observed in the last 2 decades in dairy farms which could have affected certain bulk milk quality traits. What is more, milking practices can also affect certain milk parameters; as an example, teat disinfectants containing I are used in commercial farms where pre- or postdipping is performed, leading to presence of some I in the bulk milk. However, this trace mineral is also supplied in cows' diet to fulfill their nutritional requirements, partly contributing to the milk I final concentration. Therefore, the aim of this study was to evaluate the sources of variation of milk I along with other traditional milk quality traits. A total of 91 dairy farms in northeastern Italy were enrolled in the study. In each farm, diet and bulk milk samples were collected on the same day for chemical analysis. Concentration of I, in particular, was determined in both milk and feed with gold standard. Pearson correlations were calculated among the traits available for milk and diet, and a general linear model was used to test significance of fixed effects (feeding system, milking system, farming system, herd size, herd stage of lactation, and sampling month) on milk quality traits including the I concentration. In the case of milk I, diet I and presence of I-based predipping and postdipping teat disinfect application were also tested as fixed effects. Results showed a positive linear correlation between milk and diet I content (correlation coefficient [r] = 0.78). Although milk I was also positively correlated with lactose content (r = 0.25), dietary I was not correlated with other milk traits. Milk I content was significantly affected by dietary I, I-based predipping teat disinfectant application, and herd composition. Compared with conventional farms, organic farms showed lower protein content and greater somatic cell score (SCS) but similar milk I. Milking system significantly affected only lactose content and SCS of milk. Sampling month was only significant for milk urea nitrogen and herd composition, feeding system, herd size, and herd average days in milk did not modify milk gross composition and SCS. In conclusion, dietary supply of I is the main factor affecting milk I concentration and findings suggest that I level in milk can be naturally improved in dairy cows by modulating the I content in the diet administered. However, further research is needed to evaluate the effect of I-based sanitizers on milk I.

Selected Parameters Affecting the Electricity Consumption of Automatic Milking Systems

Abstract The paper presented is aimed at the determination of electricity consumption at selected dairy farms during milking employing automatic milking systems (AMS). The research was focused on the determination of electricity consumption during the performance of essential activities that are related to the AMS utilization, i.e., electricity consumption by a single AMS unit per day, by a single assembly compressor per day, by a single AMS unit and a single assembly compressor per day, per milking, and per 1 litre of milk. Measurements were conducted at eight dairy farms in Central Europe that house dairy cows of the black-spotted Holstein cattle breed. The cattle groups assessed comprised 95 to 105 animals subjected to milking over the entire observed period. On the basis of the measurements conducted, it was observed that the average amount of energy consumed per 1 litre of milked milk was 0.0232 kWh over the entire observed period. Regarding the essential activities, the results obtained indicate that the energy consumption throughout the year is relatively balanced, despite the fact that it is affected by a wide range of factors, including chiefly the herd management, as well as overall organization of entire farm, milk yield, health status of cattle, current climatic conditions, fodder quality, etc.

Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks

To develop a body weight (BW) prediction model using milk production traits and present a useful indicator for energy balance (EB) evaluation in dairy cows. Data were collected from 30 Holstein cows using an automatic milking system. BW prediction models were developed using multiple linear regression (MLR), local regression (LOESS), and deep neural networks (DNN). Milk production traits readily available on commercial dairy farms, such as energy-corrected milk (ECM), fat-to-protein ratio, days in milk (DIM), and parity, were used as input variables for BW prediction. The EB was evaluated as the difference between energy intake and energy demand. The DNN model showed the greatest predictive accuracy for BW compared with the LOESS and MLR models. The BW predicted using the DNN model was used to calculate the energy demand. Our results revealed that the day on which the EB status transitioned from negative to positive differed among cows. The cows were assigned to one of the three EB index groups. EB index 1 indicated that the day of EB transition was within DIM ≤ 70. The EB indexes 2 and 3 were 70 < DIM ≤ 140 and 140 < DIM ≤ 305, respectively. EB index 3 had the lowest EB, which is the slowest to transition from a negative to a positive energy balance compared with EB indexes 1 and 2. The highest ECM and feed efficiency were observed for EB index 3. The calving interval was the shortest for EB index 1. EB of individual cows during lactation can be estimated and monitored with moderately high accuracy using EB indexes.

Indirect Sensing of Subclinical Intramammary Infections in Dairy Herds with a Milking Robot.

This study determined the impact of subclinical intramammary infections (IMIs), such as the major and minor udder pathogens (MaPs and MiPs), on the somatic cell count (SCC) in cow milk and investigated the possibilities of indirect sensing of the udder pathogens using the mastitis detection index (MDi) (DeLaval, Tumba, Sweden). The MDi incorporates quarter-level milk electrical conductivity, blood in milk, and milking interval. The case group (n = 21; MDi ≥ 1.4) was compared with the control group (n = 24; MDi < 1.4) for the presence of IMIs. The microbiological investigation of udder quarter foremilk samples was performed two times with an interval of 10 to 14 days. The case and control groups differed in terms of the occurrence of MaPs and MiPs in milk. During the continuous subclinical IMI and the episodic MaP infection, a higher SCC was detected compared with the episodic MiP infection or quarters without IMI. The novel finding of this study was that by using the milk quality sensor for the sensing of subclinical IMIs, there was an indication for the successful detection of episodic MaPs. However, the sensing of the continuous subclinical IMIs was not possible in the current study and still needs to be investigated.

Effect of Milking Vacuum and the Supplementation of Vitamin E and Se in Milk Quantity, Quality, and Hygiene of Mammary gland in Mountainous Greek Sheep.

The aim of this research was to study the effect of two machine milking vacuum levels on the quantitative and qualitative characteristics of milk and mammary gland hygiene of ewes, when vitamin E and Se were administrated supplementarily. The experiment was conducted at the Vlasti Research Station in the Greek province of West Macedonia. Ninety-six ewes of the Mountainous Greek sheep breed were used. Animals were separated in four equal groups of 24 ewes per group. A 2 × 2 factorial design was applied, with two milking vacuum levels (38 kPa and 46 kPa) and two rations, one supplemented with vitamin E (300 I.U.) and Se (3 mg/kg DM feed) and one without any vitamin E and Se supplementation. Six test days were assigned (evening and morning milkings) at 14-day intervals, from April to July. Following milk yield control, milk samples were collected for chemical composition and somatic cell count (SCC) determination. At the end of milking of each lot, the milk from the terminal receiver of the milking machine was received for the evaluation of total bacterial count (TBC). The results revealed that milk yield was improved considerably in the case of 46 kPa vacuum level. Moreover, the chemical composition of milk was not influenced by vacuum level; however, the administration of vitamin E and Se appeared to have a positive effect. Moreover, the addition of vitamin E and Se decreased somatic cell counts (number and log10) at the two assessed machine milking vacuum levels. In reference to TBC and their log10, significant differences were not observed at both milking vacuum levels, regardless of vitamin E and Se administration. Statistical analysis did not indicate any interactions between the factors that were studied. Therefore, it is concluded that the quantity of vitamin E and Se supplemented to the ration has a positive effect on decreasing SCC and consequent positive action in the hygiene of the mammary glands of machine milked ewes.

Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves

Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

Flow systems in robotic milking at Rio Grande do Sul state, Brazil

O uso de Sistemas de Ordenha Robotizada (SOR) tem aumentado no estado do Rio Grande do Sul (RS). O objetivo deste levantamento foi avaliar o uso dos equipamentos de SOR em atividade nas propriedades leiteiras no RS, em julho de 2022. Um questionário foi criado e enviado para as seguintes empresas que comercializavam SOR: Lely®, DeLaval® e GEA®. Verificou-se que 206 boxes de SOR estavam em atividade no estado do RS. Destes, 45 (22%) eram da marca Lely®, 140 (68%) eram DeLaval® e 21 (10%) eram GEA®. As unidades localizavam-se predominantemente na região Noroeste do estado do RS (85%). A prevalência de boxes de SOR com sistema de fluxo guiado foi superior aquelas com fluxo livre (58,3% vs 41,7%). A maior prevalência de SOR com tráfego guiado, distintamente de outras partes do mundo, sugere que as práticas de nutrição e manejo desse sistema de fluxo são relevantes para o sucesso do SOR nesta região do Brasil.

Milk characteristics and milking efficiency in Italian Mediterranean buffalo

The study aimed to verify the influence of different milking machine setting parameters on Italian Mediterranean buffalo milking performance. Data on milking machine settings (Milking Dry Tests [MDTs]) and milk Test-day (TD) were collected by the Italian Breeders Association (AIA). Data for each TD consisted of milk yield (MY), fat percentage (FP), protein percentage (PP), lactose percentage (LP) and linear score (LS). Working vacuum level (VL), pulsation ratio (PR), automatic cluster removal system (AC) and effective vacuum reserve (EVR) were recorded from each MDT. A total of 558 MDTs, 217,967 TD, collected from 43,593 buffaloes in 198 buffalo farms were utilised. MDT, TD and Animal data (AD) were analysed through a mixed linear model and a logistic regression model was used to evaluate the relationship between VL and EVR. Analysis of MY and quality revealed that an incorrect setting of the milking was responsible for a higher LS and lower MY and LP, along with higher FP and PP compared to farms with adequate EVR. Except for PP, the height of the milking system significantly influenced all milk parameters. Conversely, VL affected all milking traits. A higher PR (70:30) was responsible for significantly lower LS and higher FP compared to a 60:40 PR. Similarly, the presence of AC showed a significant effect on FP and MY and a slight reduction in LS. Finally, the diameter and length of the pipeline (DL) ratio lower than 2.5 m was associated with a decrease in LS. These results suggest that buffaloes require specific milking parameters.

Risk prediction model of clinical mastitis in lactating dairy cows based on machine learning algorithms

Mastitis is the most common disease among dairy cows and is known to have negative effects on both animal welfare and the profitability of dairy farms. Early detection of clinical mastitis cases is considered the best option for preventing cows from developing mastitis. In this study, we developed clinical mastitis prediction models that only required inputting common indicators from the automatic milking system. We utilized multidimensional data from the cow mastitis database of Afimilk (China) Agricultural Technology Co., Ltd. to predict mastitis in dairy cows. All data were screened for the period of 0–150 days of lactation. The data included parity, lactation day, period, mean and standard deviation of milk yield, of electrical conductivity, and of lying time, which were taken as input features. The classification of whether cows suffer from clinical mastitis was determined as output. We analyzed 426 cows with clinical mastitis and 2087 healthy cows by using four machine learning algorithms: Decision Tree, Random Forest, Back Propagation neural networks, and Support Vector Machines. In these four algorithms, the accuracy ranged from 94% to 98%, while the running times varied widely from seconds to minutes. The decision tree prediction model achieved an accuracy of 98% and the precision rate for healthy cows was 99%, while for mastitis cows it was 97%. Machine learning algorithms have played an important role in predicting cow mastitis, with the Decision Tree algorithm showing great performance and higher accuracy in our research.

Macbeth ve Gri İlişkisel Analiz Yöntemleri İle En Uygun Süt Sağım Makinesinin Belirlenmesi

Milking machines are an important element of the livestock sector, which is one of the main activities of the countries. Milking machines have become a part of the life of livestock keepers. Such equipment can be considered as applications of mechanical engineering on the livestock sector. Especially livestock enterprises are going through a difficult process in supplying such machines with optimum features and maximum benefit. In terms of productivity, competitiveness and sustainability of livestock sector enterprises, decision-making processes should be scientific. With this perspective, in this study, the problem of determining the optimum milking machine was evaluated with Multi-Criteria Decision Making (MCDM) methods. In the study, six different milking machines were analyzed with two different MCDM methods according to eight criteria. In this frame, the criterion weights of the related decision problem were calculated by the MACBETH method. Moreover, MACBETH and Gray Relational Analysis (GIA) methods were used separately to determine the most suitable milking machine. Furthermore, rankings obtained+- by different methods were tested with Spearman Rank Correlation Analysis and the result was found to be highly positive. The results of the study were shared with the decision makers. Besides, academic, and sectoral suggestions were made for future studies on similar topics.

Time budgets of dairy cows in a cow-calf contact system with automatic milking

Daily time budgets can be used to determine the amount of time cows allocate to different behaviors throughout the day and can be useful when evaluating housing and management systems. There is a growing interest in keeping dairy cows and calves together during the first months of lactation; however, it is not known how their time budgets are affected by the calf contact. The aim of this study was to investigate differences in time budgets between 2 groups of dairy cows housed in the same pen within a freestall system with automatic milking. One group of cows had access to their calves until 4 mo of lactation, whereas the other group had no direct contact with the calves. Using focal animal sampling by video analysis and sensor data available from the milking unit we determined the 24-h time budgets of 37 dairy cows. The sample consisted of 20 primi- and 17 multiparous cows of the breeds Swedish Red and Swedish Holstein. The cows were randomly assigned either cow-calf contact or no contact, with separation from the calf within 24 h postparturition. Data were collected when cows were on average 43, 75, and 107 d in milk. The results showed that the cow-calf cows spent less time eating silage, without reduced dry matter intake, less time socializing with other cows, and less time standing in cubicles. However, the duration spent in the waiting area in front of the milking unit was greater among the cow-calf cows. In conclusion, neither cow-calf contact nor no-contact cows seemed to be constrained in any of the included behaviors to a point of reduced welfare; hence, a cow-calf contact system in combination with automatic milking may be possible to set up on farms with maintained or improved animal welfare.

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.

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.

Cows and Humans as Technology Users: Multispecies Agency and Gender in Automated Milking Systems in Finland.

Automatization has changed the interaction between cows and humans in subtle ways. Automated milking systems (AMS) have been used commercially since the early 1990s, first in the Netherlands, then in Western Europe and North America, before reaching Finland. By 2021, nearly 27 percent of Finnish dairy farms have adopted them. Based on fieldwork at AMS farms and Finnish trade journal items, this article argues that as technology users, both humans and cows are engaged in agential entanglements with milking robots. It highlights challenges that cows and farmers face in learning to use the milking robot, how it impacts their working and living rhythms, and how the technologized work with AMSs is gendered among dairy farmers.

Calf Health, Feeding and Social Behaviours within Groups Fed on Automatic Milk Feeders

Automatic calf feeders (AMFs) that supply milk for pre-weaning calves require group housing. This observational study assessed calf growth, health, feeding patterns and social behaviours prior to, during and following weaning in groups on the AMF. Calves were recruited from a single UK dairy farm and placed into two groups on the AMF between 3 and5 weeks of age (n = 19 and 13). They were fed milk near-ad libitum until 8 weeks of age when they entered a 2-week step-down weaning period. Daily milk consumption and the numbers of successful and refusal visits were collected from the AMF, along with weekly weights and health checks. Calf behaviour around the AMF was monitored through video analysis, and activity was assessed using tri-axial accelerometers. On average, the calves consumed approximately 3 L of milk three times a day from the AMF, confirming that limiting calves to twice daily feeding will restrict their desired milk consumption. The ADLG pre-weaning was 0.74 kg/d (SD 0.41 kg), rising to 0.99 kg/d (SD 0.46 kg) over weaning and 1.05 kg/d (SD 0.12 kg) post weaning. Refusal visits to the AMF, social interactions, displacements and cross sucking all increased over the weaning periods, suggesting that the calves were experiencing frustration due to a lack of expected milk rewards rather than hunger as ADLG continued to increase. Female calves also had more successful and refusal visits to the AMF than males, along with higher displacement indexes (p = 0.052), suggesting single-sex groups may be beneficial to AMFs. Calf activity had a diurnal pattern, with the highest standing times being during mid-morning and early evening, which may put pressure on limited resources if all calves are active during these particular time periods.

ОСОБЕННОСТИ ДИНАМИКИ И СТРУКТУРЫ ПРОФЕССИОНАЛЬНОЙ ЗАБОЛЕВАЕМОСТИ В РЕСПУБЛИКЕ ТАТАРСТАН

Introduction. In their labor activities, employees are exposed to some hazardous and harmful factors of the working environment, of which the key ones are physical factors, such as noise or local/general vibration; physical and functional overloads of the musculoskeletal system; aerosols of predominantly fibrogenic action; and dust. The core indicator characterizing the occupational risk of a health problems is occupational morbidity. Aim. Studying the time profile and structure of occupational morbidity in employees of the Republic of Tatarstan. Material and methods. Occupational morbidity over the years 2007-2021 was analyzed. Over the period under research, occupational illnesses were diagnosed in 2,546 employees of 130 occupational categories. Results and discussion. The highest rates of occupational morbidity were recorded among employees of agricultural and mechanical engineering enterprises and companies. About 60% of occupational diseases were diagnosed in those pursuing 7 professions. What stands out is the similarity of occupational pathologies in assembly workers and riveters, fettlers, and farm-machine operators, who were diagnosed with vibration syndrome and occupational deafness. Occupational diseases of respiratory and musculoskeletal systems were additionally diagnosed in fettlers. In milkmaids, the occupational diseases of musculoskeletal system take the leading place. Polymorbidity of occupational pathology is most pronounced in machine milking operators followed by tractor drivers and fettlers. Conclusion. It is necessary to ensure early occupational pathology diagnoses in routine health screenings and timely referrals to the occupational pathology center in order to perform causal relationship assessments between disease and occupation.

The effect of 2 different premilking stimulation regimens, with and without a latency period, on teat tissue condition and milking performance in Holstein dairy cows

The objectives of this study were to assess the effect of 2 different premilking stimulation regimens, with and without a latency period between tactile stimulation and the attachment of the milking unit, on the teat tissue condition and milking performance of dairy cows. In a randomized controlled crossover study, 145 Holstein cows milked 3 times daily were assigned to treatment (TRT) or control (CON) groups. Premilking udder preparation for the TRT group consisted of the application of a latency period resulting in a preparation lag time of 90 s. The only difference in the premilking udder preparation of the CON group was the absence of latency period; the milking unit was attached immediately after completion of the tactile stimulation. The average duration of total tactile stimulation in TRT and CON group was 8 ± 2 and 9 ± 2 s, respectively. The study lasted for 14 d and was split into 2 periods, each consisting of a 2-d adjustment period followed by 5 d of data collection. We assessed machine milking-induced short-term changes to the teat tissue by palpation and visual inspection postmilking. Electronic on-farm milk meters were used to assess milking characteristics (milk yield [kg/milking session], machine-on time [s], 2-min milk yield [kg], and duration of low milk flow rate [s]). Generalized linear mixed models were used to analyze the effect of treatment on the outcome variables. The odds of machine milking-induced short-term changes to the teat tissue were lower for cows that received a 90-s preparation lag time (TRT cows) compared with cows in the CON group (odds ratio [95% confidence interval; 95% CI] = 0.13 [0.08-0.20]). The least squares means (95% CI) values of cows in the TRT and CON groups were 15.4 (14.9-15.9) and 15.3 (14.8-15.8) kg, respectively, for milk yield, and 246 (239-253) and 253 (247-260) s for machine-on time. The 2-min milk yield was higher for the TRT compared with CON group cows at all the parity levels. The 2-min milk yields of animals in lactation 1, 2, and ≥3 were 5.7, 5.7, and 6.5 kg, respectively, in the TRT group and 4.6, 5.0, and 5.9 kg in the CON group. The TRT cows spent less time in low milk flow rate compared with CON cows at all parity levels. The durations of low milk flow rate of cows in lactation 1, 2, and ≥3 in the TRT group were 19, 17 and 13 s, respectively, and those in the CON group were 31, 22, and 15 s. In this study, cows that received a latency period, and thus were subjected to a 90-s preparation lag time had lower odds of exhibiting short-term changes to the teat tissue after machine milking, shorter machine-on time, higher 2-min milk yields, and lower durations of low milk flow rates. We conclude that consideration of latency period leading to a 90-s preparation lag time in the premilking stimulation regimen facilitated cows' milk-ejection reflex. This latency period can alleviate the adverse effects of vacuum-induced forces on teat tissue during machine milking, improve udder health, and promote animal well-being.

A comparative study of social behavior in primiparous and multiparous dairy cows during automatic milking

In dairy farming, social behavior plays a critical role in ensuring welfare and productivity of cows. Understanding social associations in dairy commercial farms could help farmers in creating herd management practices able to consider individual animal needs, in particular in stressful conditions as during regrouping. Here, we investigated the social behavior of 150 dairy cows during milking events in a free traffic barn at an Automatic Milking System (AMS) and we compared their social structure between their first lactation (when they were housed in the primiparous area) and their subsequent lactations in the multiparous area. Data on individual milking traffic and daily milk yield were collected for a period of 5 years and 5 months. To identify and analyze consistent social associations among cows, we utilized the time interval between milking events, which is defined as the time difference between the entry time of one cow and the entry time of the next in the milking robot. Our findings revealed significant social differentiations in both areas, indicating the presence of non-random social structures within the herd. During milking, younger cows exhibited a tendency to form stronger social associations with particular individuals based on genetic relatedness. The correlation observed between genetic relatedness and social behavior in young cows indicates that grouping genetically related individuals could provide advantages. On the other hand, as cows age, they tend to exhibit a higher degree of social connectivity with their herd mates. This suggests that older cows have a wider range of social associations which is not driven by genetic relatedness. We examined the potential influence of social associations on cow productivity, and we found no significant correlation between social behavior and milk yield for primiparous and multiparous cows. At a temporal level, we compared their ego-networks, when they were hosted in the primiparous area and when they were hosted in the multiparous area, and we found a significant level of social stability. Although the factors that influence social behavior in cows may differ with age, our findings indicate the tendency to maintain consistent social relationships over time. Maintaining stable relationships is crucial for enhancing the welfare of cows in social contexts, and this knowledge can promote the development of management practices aimed at supporting their social well-being. Our study highlights the importance of understanding social behavior and dynamics in dairy cows and offers valuable insights that can guide the development of effective herd management practices in the dairy farming industry.

Effects of 2 gradual debonding strategies on machine milk yield, flow, and composition in a cow-driven cow-calf contact system

This controlled study compared the effects of 2 different gradual debonding strategies on machine milk yield, flow, and composition in a cow-driven cow-calf contact (CCC) system with automatic milking. Cows had 24 h/d access to their calves during the first weeks of lactation. In the long debonding (LDB) treatment (n = 16), a gradual reduction of cows' access to their calves was initiated 4 wk after calving over a total period of 28 d; first to 12 h/d (14 d), and then to 6 h/d (14 d). In the short debonding (SDB) treatment (n = 14), gradual reduction was initiated 6.5 wk after calving over a total period of 10 d; first to 12 h/d (5 d), and then to 6 h/d (5 d). From 6 h/d, access was finally reduced to 0 h/d for 7 d for both treatments. Machine milk yield, somatic cell count, and peak and average milk flow were automatically registered at milking. During the 9-wk study period, composite samples were analyzed for milk composition. Data were analyzed with linear mixed effect models. Results showed that machine milk yield during 24 h/d access varied between cows (range 1.2-49.9 kg/d, average ± standard deviation 13.2 ± 7.82 kg/d). The LDB cows had a higher daily machine milk yield than SDB cows at the end of and after access reduction was completed (+5.0 ± 1.63 and +5.1 ± 1.55 kg during the last 5 d of 6 h/d access, and 0 h/d access, respectively). Somatic cell count was on a healthy level, with no difference between treatments. Milk fat content increased with reduction in access, regardless of treatment. Short debonding cows tended to show higher milk protein content and lower milk lactose content than cows with a longer debonding. This study has shown that a longer debonding initiated earlier may give a higher milk yield in the short term. The variation in machine milk yield may indicate differences in milk ejection, suckling, and visiting patterns and preferences among cows.

Behavior of Cows in the Lying Area When the Exit Gates in the Pens Are Opened: How Many Cows Are Forced to Get Up to Go to the Milking Parlor?

Equipping a farm with a milking parlor requires moving groups of cows from their pens to the part of the barn where milking takes place. The task of moving cows, carried out two or three times each day, shows links to the lying area of the barn. When the cows are taken from the pen to the milking parlor, some of them may be lying down, and this lying down must be interrupted. The forced standing up of cows can be considered in terms of their welfare. The aim of the study was to examine the number of cows lying in the stalls at the time of opening the exit gates in the pens in order to take the cows to the milking parlor. The study covered four pens, each with 12 cows. The behavior of the cows in the pens before morning and afternoon milking was recorded over 26 days. In the analysis, the dependent variable was the number of lying cows, and the independent variables were the time of milking and the level of sand in the lying stalls. The results of the study showed the significance of differences in the number of lying cows for stalls with a low and high level of sand, both in the case of morning and afternoon milking. Differences in the number of lying cows were also found when comparing the time before morning and afternoon milking. To compare the tendency of individual cows to lie down before going to milking, an index of forced standing up was proposed. The research conducted showed differences in the behavior of cows before leaving the pen to the milking parlor. The stage to reduce the forced standing up of cows is to equip the farm with an automatic milking system (AMS) instead of using a milking parlor. In barns with AMS, cows have full freedom to get up and approach the milking stall. The results of the observations are thus an additional argument confirming the benefits of using an automatic milking system, considered in terms of the welfare of dairy cows, regarding their lying down and getting up.