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Copyright of Summa, Animali da Reddito is the property of Point Veterinaire Italie s.r.l. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper shows the development of technologies and tools of mechanization and automation in livestock farming for the period from 1930 to the present. (Research purpose) Given the cyclical nature of processes and the nuanced interpretations of scientific heritage, it is necessary to investigate the historical experience and contributions of scientists to the development and practical implementation of mechanization and automation tools in livestock farming. (Methods and materials) The paper examines the primary stages of mechanization and automation in cow milking, starting with the development of the first Soviet three-stroke milking machine DA-3. It considers the contributions made by specialized institutes of the All-Russian Academy of Agricultural Sciences and individual scientists in formulating testing methodologies for milking machines. Additionally, it explores the development and adoption of novel electromechanical tools for dairy farms. (Results and discussion) Considered in this paper are the pioneering efforts in the creation of the first milking parlors. These include the stationary herringbone type machines with a parallel-pass design, spearheaded by V.S. Krasnov, V.F. Korolev, V.P. Larin, V.P. Pokhvalensky, and A.N. Dormidontov, and the development of mobile carousel parlors of the «rotating herringbone» type, engineered by the design bureau of the Siberian Research Institute of Agriculture under the supervision of I.I. Teslenko, N.V. Krasnoshchekov, K.S. Shapovalov, N.K. Vazenmiller, A.V. Goldenfang. (Conclusions) The paper notes the significance of adopting a systematic approach to the developing of machinery for livestock farming mechanization. This approach was actively pursued by N.M. Morozov, the member of the Russian Academy of Sciences. A significant milestone in the advancement of the theory and practice of animal husbandry processes was the pioneering work in developing and implementing technology for flow-conveyor animal service, headed by Member of the Russian Academy of Sciences L.P. Kormanovsky. The paper reveals the necessity of conducting a historical practices from previous generations. This approach helps to develop modern models of machinery and equipment and avoid mere replication of foreign designs. It also facilitates independent research and development efforts, incorporating insights from the accumulated experience of utilizing equipment and technologies in Russia.

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.

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

ABSTRACT: After calving, high-yielding dairy cows mobilize body reserves for energy, sometimes to the detriment of health and fertility. This study aimed to estimate the genetic correlation between body weight loss until nadir and daily milk production (MY24) in first- (L1) and second-lactation (L2) Holstein cows. The data set included 859,020 MY24 records and 570,651 daily raw body weight (BWr) phenotypes from 3,989 L1 cows, and 665,361 MY24 records and 449,449 BWr phenotypes from 3,060 L2 cows, recorded on 36 French commercial farms equipped with milking robots that included an automatic weighing platform. To avoid any bias due to change in digestive content, BWr was adjusted for variations in feed intake, estimated from milk production and BWr. Adjusted body weight was denoted BW. The genetic parameters of BW and MY24 in L1 and L2 cows were estimated using a 4-trait random regression model. In this model, the random effects were fitted by second-order Legendre polynomials on a weekly basis from wk 1 to 44. Nadir of BW was found to be earlier than reported in the literature, at 29 d in milk, and BW loss from calving to nadir was also lower than generally assumed, close to 29 kg. To estimate genetic correlations between body weight loss and production, we defined BWL5 as the loss of weight between wk 1 and 5 after calving. Genetic correlations between BWL5 and MY24 ranged from −0.26 to 0.05 in L1 and from −0.11 to 0.10 in L2, according to days in milk. These moderate to low values suggest that it may be possible to select for milk production without increasing early body mobilization.

The article presents results of monitoring carried out in barns with milking robots. The use of milking robots makes it possible, with proper stocking, to milk cows without human intervention. The analysis included all barns with Lely robots located in the Podlaskie Voivodeship in 2018–2021. In 2018–2019, there were seven such barns, and in 2020–2021 nine. In all barns, high milk yields were obtained of more than 1000 kg compared to the average milk yield obtained from stock of cows under monitoring in the Podlaskie Voivodeship. In 2021, four barns milked more than 9.5 thous. kg, three barns more than 10 thous. kg and two barns almost 12 thous. kg of milk. Fat and protein contents were typical for the breed. Three barns were monitored more closely in 2021, with varying numbers of robot milking stations in barns, i.e. A one, B two and C three milking stations. In 2021, over 700,000 kg was milked per stall in stall A, over 750,000 kg in B and over 850,000 kg of milk in C. The average milk per cow per milking was high, with over 11 kg in barn A, 12 kg in B and 13 kg in C.

ABSTRACT: Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01–0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88–0.96) than between lactations 1 and 2 or 3 (0.34–0.88) for dPert, LnVar, and rauto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and rauto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.

Copyright of Gazdalkodas is the property of Herman Otto Intezet Nonprofit Kft. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper shows the main ways of machine technology development from manual milking to robotic and notes the ambiguity of expert assessments regarding the introduction of robotic milking. (Research purpose) To conduct historical, analytical and feasibility studies of various options for automating cow milking on dairy farms, to determine the overall capital intensity of various options for the transition from manual to automated and robotic cow milking technologies, as well as the capital intensity of individual transition stages such as mechanized bucket milking, pipeline milking, automated milking in milking parlors, robotic milking in individual boxes and milking parlors. (Materials and methods) The paper analyzes the revolutionary transitions associated with a drastic change in technology, a change in the way of keeping cows and a change in the organizational and economic forms of operations; the modernization transitions, those with a complete or partial replacement of equipment but without a fundamental change in technology; and the reverse transfer transitions - a forced return to the previous more economical technology. (Results and discussion) We considered 7 main ways of developing machine milking over 100-110 years from the manual milking method to the automated and robotic ones. It is determined that their total estimated capital intensity for a hypothetical reference farm per 1000 heads accounts for 78.0-474.5 million rubles in modern figures. The concept of a standardized unit is introduced, that is equal to the price of the DAS-2V milking unit, that is, approximately 0.2 million rubles at the moment. (Conclusions) The research reveals the most costly transition ways, the optimal least capital-intensive way of transition from traditional milking to automated quarter-by-quarter milking in parlors, whose functionality is up to the robotic milking.The modernization transition to robotic milking in parlors is proved to be possible by gradual replacement of the automated milking manipulators controlled by an operator with the robotic unmanned manipulators and without any changes in technology.

After calving, high-yielding dairy cows mobilize body reserves for energy, sometimes to the detriment of health and fertility. This study aimed to estimate the genetic correlation between body weight loss until nadir and daily milk production (MY24) in first- (L1) and second-lactation (L2) Holstein cows. The data set included 859,020 MY24 records and 570,651 daily raw body weight (BWr) phenotypes from 3,989 L1 cows, and 665,361 MY24 records and 449,449 BWr phenotypes from 3,060 L2 cows, recorded on 36 French commercial farms equipped with milking robots that included an automatic weighing platform. To avoid any bias due to change in digestive content, BWr was adjusted for variations in feed intake, estimated from milk production and BWr. Adjusted body weight was denoted BW. The genetic parameters of BW and MY24 in L1 and L2 cows were estimated using a 4-trait random regression model. In this model, the random effects were fitted by second-order Legendre polynomials on a weekly basis from wk 1 to 44. Nadir of BW was found to be earlier than reported in the literature, at 29 d in milk, and BW loss from calving to nadir was also lower than generally assumed, close to 29 kg. To estimate genetic correlations between body weight loss and production, we defined BWL5 as the loss of weight between wk 1 and 5 after calving. Genetic correlations between BWL5 and MY24 ranged from −0.26 to 0.05 in L1 and from −0.11 to 0.10 in L2, according to days in milk. These moderate to low values suggest that it may be possible to select for milk production without increasing early body mobilization. [ABSTRACT FROM AUTHOR]

Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (r auto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01–0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88–0.96) than between lactations 1 and 2 or 3 (0.34–0.88) for dPert, LnVar, and r auto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and r auto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes. [ABSTRACT FROM AUTHOR]

The article presents results of monitoring carried out in barns with milking robots. The use of milking robots makes it possible, with proper stocking, to milk cows without human intervention. The analysis included all barns with Lely robots located in the Podlaskie Voivodeship in 2018-2021. In 2018-2019, there were seven such barns, and in 2020-2021 nine. In all barns, high milk yields were obtained of more than 1000 kg compared to the average milk yield obtained from stock of cows under monitoring in the Podlaskie Voivodeship. In 2021, four barns milked more than 9.5 thous. kg, three barns more than 10 thous. kg and two barns almost 12 thous. kg of milk. Fat and protein contents were typical for the breed. Three barns were monitored more closely in 2021, with varying numbers of robot milking stations in barns, i.e. A one, B two and C three milking stations. In 2021, over 700,000 kg was milked per stall in stall A, over 750,000 kg in B and over 850,000 kg of milk in C. The average milk per cow per milking was high, with over 11 kg in barn A, 12 kg in B and 13 kg in C. [ABSTRACT FROM AUTHOR]

Milking is a routine dairy business that has physical and biological aspects. Today, by means of technological improvements, milking can be performed without human intervention in dairy cattle enterprises. To achieve high levels of milk production, the dairyman must use good milking techniques and milking systems that will milk cows efficiently. In this research, work principles and equipments of robotic milking system, consequence in labour, milk production and milk quality of robotic milking were discussed. [ABSTRACT FROM AUTHOR]

Copyright of Agronomía & Ambiente is the property of Universidad de Buenos Aires. Facultad de Agronomia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• Eight novel udder health parameters were calculated based on test-day SCC results. • Udder health was considered to be poor on most farms. • Farm size, year and season influenced udder health performance on farms with AMS. • Milk production was negatively associated with the udder health parameters. Despite the increasing use of automated milking systems (AMS), udder health performance of farms has only been limitedly quantified on farms with a milking robot. The objective of this study was to quantify udder health parameters (UHP) at test-day and at herd level on robotic farms using routinely collected somatic cell counts (SCC) from test-day data, and to study the correlations between these UHP. Additionally, the associations between UHP and season, year and farm size as well as the association between the daily milk production and the herd milk SCC were investigated. To this end, we collected SCC test-day data from 48 Belgian and Dutch farms participating in the milk recording of a local dairy herd improvement program. The UHP were calculated based on individual animal records at each test day over three years (2017 until 2019). These UHP included herd milk SCC, average heifer SCC, average cow SCC, percentage of high SCC, percentage of new high SCC, percentage of chronic high SCC, percentage of high SCC cows after calving, and percentage of recovery during the lactation. By fitting a linear mixed model effect for each of the different UHP, significant associations between the UHP and season, year and farm size were observed at test-day level. A poorer udder health was observed during summer compared to other seasons and generally, larger farms performed worse than smaller farms. Using a linear regression model, a significant negative correlation between herd average SCC and average daily milk production was found: an increase of 6,085 cells/mL was associated with a decrease of one kg of milk production. Finally, the cow-level UHP were averaged over all test days of the entire study period per farm to investigate the farm-level udder health performance and correlations between them. At the farm level, UHP analysis revealed most farms had poor udder health performance (i.e. the herd milk SCC, average heifer SCC, average cow SCC, percentage of new high and chronic high SCC were all higher than their target values) as proposed by Barkema et al. (2013). Both at the farm and at test day level, strong correlations were found among the UHP. Similar to studies on farms with conventional milking systems, a strong correlation between herd milk SCC and the percentage of new and chronic high SCC cows was found. This study gives new insights into udder health performance on farms with an AMS and provides veterinarians and other dairy industry stakeholders with concrete benchmarks for udder health performance on farms. [ABSTRACT FROM AUTHOR]

The article deals with the determination of water consumption at selected dairy farms during milking using automatic milking systems (AMS). The aim was to determine the water consumption during basic activities associated with the AMS use, namely water for cleaning the milking set and water for cleaning and disinfecting the entire milking system at set daily intervals (hereinafter water). The research was carried out at seven farms in the Czech Republic, which house dairy cows of the black-spotted Holstein cattle. The evaluated groups of dairy cows had from 97 to 103 milked cows for the entire monitored period. Based on the performed measurements, it was found that the average amount of water consumed by AMS per 1 litre of milked milk was 0.33 litres. The results show that water consumption is not completely constant during the year. Multiple factors, including performance, the number of non-drinking milk detections, animal health and current climatic conditions, influence it.

The first milking robots appeared on the European market in the early 1990s. Robotic Milking Systems (RMS), designed to make bovine milking voluntary, are constantly evolving. Manufacturers currently share the market, with fixed or mobile single or multi-stall machines. The installation of a milking robot in dairy barns represents new challenges for the industry in terms of meeting quality milk production standards and optimizing robot performance. But the future of the robot also depends on the technical progress to be made in the detection of abnormal milk and by maintaining, or even strengthening, its use with grazing cows, so as not to harm the quality image of the product and animal welfare that consumers have from the dairy chain. Thus, milking robot is an automaton, programmable equipment that allows dairy cows milking without direct intervention of the farmer: all proceedings are fully automated, in particular the installation of the milking cluster. Robotic milking uses largely the same technologies as conventional installations. The milking robot facilitates the collection of a huge data via several sensors, and stores it, to allow the farmer making decisions more easily. It is more than just a milking technology that improves the daily life quality, it is a new way of conceiving the conduct of a herd. The milking robot exploitation helps to manage milking, milk quality but also the feeding of animals, their circulation in the building and their access to pastures. Although the impact of this technology on labor organization and herd management was highlighted, particular attention was paid to milk quality degradation and the increase of somatic cells concentration (SCC) in the herd milk. Furthermore, the milking robot involves the inter-reaction of comfort, animal health and work efficiency. [ABSTRACT FROM AUTHOR]

An increasing number of farmers want to use the advantages of both grazing and automated milking systems. Oftentimes, selection gates are used to regulate the access of cows to pasture based on their specific milking permission. The aim of our study was to identify preferences of cows to help modify AMS systems with pasture to better serve animal needs. We used individual animal identification on one study farm to evaluate cows' visits of the automated milking system and the selection gate. Our data shows that the visits were dependent on time of day and lactation number of the animals. The highest number of visits at the selection gate occurred during the evening and at night. The cows went to pasture voluntarily for an average of 6,3 hours per day in one visit. The number of visits ranged from none to 3,5 per day with a total duration of pasture use of up to almost 18 hours. Our results give valuable insights into possible ways to improve the regulation of the access to pasture and milking permission. Moreover, they offer an initial starting point to establish practical requirements with regard to the management of grazing. [ABSTRACT FROM AUTHOR]

The effect of high air temperature on clinical and hematological parameters in lactating cows during voluntary and forced milking when they are kept in frame cowsheds made of steel structures was studied. During forced (milking parlor) and voluntary (milking robot) milking the heart rate of cows increased by 5 and 15 beats per minute, respectively, and the number of respiratory movements increased from 50 to 77 at high air temperature in the cowshed that is 1.7 and 2.1 times higher than at optimal values of this microclimate indicator. It was identified that the body surface temperature of cows depended on the air temperature in the cowshed during forced milking and, at high air temperature, it increased to 35.8–37.9 °C, which was 6.9– 7.5 °C above its optimal value, while this indicator of the udder increased from 31.6– 33.3 °C up to 32.3–38.5 °C. Humidification and irrigation of body surface in lactating cows in combination with increasing the airspeed to 1.2 m/s in the storage reduced body surface temperature of lactating cows to 33.2–34.5 °C that an average turned out to be by 2.5–4.7 °C lower than similar indicators for animals in the cowshed. The hemoglobin concentration in the blood of lactating cows is lower by 15.6 g/L, the number of band neutrophils – by 3.9% and monocytes – by 2.6% under high air temperature compared with the optimal. During voluntary milking at high temperatures compared with the optimal, the hemoglobin content in the blood was lower by 47.2 g/L, the number of white blood cells – by 1.95 g/L, and monocytes – by 6.25%

In Sweden, 34% of herds in official statistics 2021 (77% of the cows) have an automatic milking system (AMS) and keep 19% of the dairy cows. This study should be considered in relation to the rapid increase of digitalisation in agriculture. It aimed at investigating Swedish farmers' experiences and reflections in dairy farming concerning AMS use from a care perspective, based on two research questions: 1) What kinds of success factors and management challenges do farmers experience with AMS usage? and 2) How do farmers view their work environment in this kind of system? A mixed method approach was performed, using method triangulation through a questionnaire, interviews, and field visits. The Activity Theory (AT) was used as a theoretical lens to consider care practice in the dairy farming as a learning system. AND CONCLUSIONS: Participating dairy farmers were found to be in a continuous learning process on different levels in their system, from detailed problems with an individual cow or the herd to the whole dairy system. Implementation of AMS required learning in order to manage, and thus care for, a system comprising of animals, technology, and humans, to increase business viability. In successful AMS use, willingness to learn, adapt to the local situation, and continually improve practice, or care as a patterning of activities, appeared to be the most important factors. With more people involved, differentiations were possible, which in turn accentuated the need for more trained staff who can perform more complicated tasks. The findings indicated high importance of experience and a 'stockperson's eye', in combination with tool-mediated seeing using data from the robot, in developing enhanced professional vision and good care. A good stockperson had broad competence combining a stockperson's eye with experience with robot data. One of the greatest challenges for dairy farms was finding a good stockperson as staff or advisor. Increased flexibility in work and better physical health were important driving forces for implementing AMS, while handling alarms was mentally stressful and gave different perspectives on AMS vulnerability. Overall, the analysis of the collected data showed that AMS had brought major, primarily positive, changes in daily work and increased work satisfaction for most farmers, with a clear majority of the respondents feeling good in their work situation and enjoying their work. Application of AT in studying AMS from a care perspective, represents a shift from traditional research that normally addresses technological inventions, to studying farmers' socio-technical system. The AT lens revealed the work practices in performing care, as a patterning of activities accomplished by a tinkering learning process, in the rich and messy matrix of humans, cows, and technology. • Farmers with automated milking systems are mainly satisfied with the robots [77]. • Finding competent staff and advisors are major challenges in robot dairy production [85]. • The importance of a stockperson's eye increases in automated milking systems [78]. • The Activity Theory works well for analysis of the systemic perspective of care [81]. • There is a need for an Agriculture 5.0, which has a more profound care perspective [84]. [ABSTRACT FROM AUTHOR]

The objective of the study was to evaluate the effect of hygiene measures in automatic milking units on the transmission of 3 mastitis pathogens considered to be mainly or partly transmitted from cow to cow during milking events. Two studies were conducted as within-herd experimental trials in 2 Danish commercial dairy herds (A and B) with automatic milking systems. Interventions to enhance hygiene were implemented on the automatic milking units. The 2 studies evaluated separate interventions. In herd A, the hygiene interventions were manual wash with the Lely foam unit and adjustments on the brush-mediated teat cleaning procedure. In herd B, the hygiene intervention included automatic disinfection spray on the upper surface of the brush motor and daily change of brushes. Composite milk samples were collected longitudinally at 3- or 4-wk intervals from all lactating cows. Additional milk samples were taken from cows entering or leaving the study groups. Milk samples were analyzed with quantitative PCR. A hidden Markov model implemented within a Bayesian framework was used to estimate the transmission probability. For analysis, 701 samples from 156 cows were used for herd A, and 1,349 samples from 390 cows were used for herd B. In the intervention group in herd B, transmission of Streptococcus agalactiae was reduced to 19% (95% posterior credibility interval: 0.00–64%) of the transmission in the control group, whereas transmission of Streptococcus dysgalactiae was reduced to 17% (95% posterior credibility interval: 0.00–85%) of transmission in the control group. This suggests that automatic spray on the upper surface of the brush motor with disinfectant along with daily change of brushes collectively reduced transmission of Strep. agalactiae and Strep. dysgalactiae. Results on Staphylococcus aureus in herd B and results on manual foam cleaning and brush-mediated teat cleaning adjustments in herd A were inconclusive. [ABSTRACT FROM AUTHOR]

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

The effect of high air temperature on clinical and hematological parameters in lactating cows during voluntary and forced milking when they are kept in frame cowsheds made of steel structures was studied. During forced (milking parlor) and voluntary (milking robot) milking the heart rate of cows increased by 5 and 15 beats per minute, respectively, and the number of respiratory movements increased from 50 to 77 at high air temperature in the cowshed that is 1.7 and 2.1 times higher than at optimal values of this microclimate indicator. It was identified that the body surface temperature of cows depended on the air temperature in the cowshed during forced milking and, at high air temperature, it increased to 35.8-37.9 °C, which was 6.97.5 °C above its optimal value, while this indicator of the udder increased from 31.633.3 °C up to 32.3-38.5 °C. Humidification and irrigation of body surface in lactating cows in combination with increasing the airspeed to 1.2 m/s in the storage reduced body surface temperature of lactating cows to 33.2-34.5 °C that an average turned out to be by 2.5-4.7 °C lower than similar indicators for animals in the cowshed. The hemoglobin concentration in the blood of lactating cows is lower by 15.6 g/L, the number of band neutrophils - by 3.9% and monocytes - by 2.6% under high air temperature compared with the optimal. During voluntary milking at high temperatures compared with the optimal, the hemoglobin content in the blood was lower by 47.2 g/L, the number of white blood cells - by 1.95 g/L, and monocytes - by 6.25%. [ABSTRACT FROM AUTHOR]

Automatic milking systems (AMS) have aroused worldwide interest recently. The first installation was by the company Lely in a project in the Netherlands (its homeland) in 1992. But nowadays, AMS represents a growing reality due to lobby for labor issues, rising costs, difficulty finding well-trained workers, and/ or difficulty keeping people on farms. This work aimed to present a review of the literature on AMS, beginning with a brief history of the evolution of the technology, showing advantages and limitations of its use, and ultimately giving some suggestions. The understanding of the technical functioning and operational running can help farmers and technicians in decision making on the adoption of the new technology. Besides workforce reduction and labor quality promotion, AMS has potential to improve feed conversion to milk, milk quality (with lower SCC), and cow productivity, as well as providing useful data and parameters for better farm management. Potential limitations include high investment costs, changes in milk composition (solids, free fatty acids), and increased risk of ketosis in cows.

In the current dairy industry, an intensive demand for estrus detection and early diagnosis of pregnancy has been increasing. Progesterone concentration in bovine milk is used as an important indicator of estrus detection and early pregnancy diagnosis. Current method for milk progesterone determination is destructive in nature. In contrast, near-infrared spectroscopy (NIRS) is a non-destructive analytical method that can be used for milk quality determination but there has been limited study on using NIRS for milk progesterone concentration during milking. Thus, the objective of this study was to develop an online real-time NIR spectroscopic sensing system for milk progesterone determination during milking by using a specific enzyme immunosorbent assay as a reference (chemical) method. Milk spectra with a wavelength range of 700 to 1050 nm and milk samples were collected every 20 s during milking from four lactating Holstein cows for 28 days using the NIR spectroscopic sensing system. Calibration models were developed using partial least squares analytical method and the precision and accuracy of the models was validated. Milk progesterone concentration for each milking was calculated by taking the progesterone concentration of the milk predicted values and milk yield obtained every 20 s, and was compared with the milk progesterone concentration chemical analysis value for one milking (bucket milk). The results obtained show that one milking time measurement accuracy of progesterone concentration was reasonably good. By installing the NIR spectroscopic sensing system developed in this study into a milking robot, it could predict milk progesterone concentration for one milking with almost the same accuracy as chemical analysis. Thus, recording this predicted value every milking and monitoring the continuous transition of the milk progesterone concentrations, it becomes possible to use it for the detection of estrus status and for the diagnosis of pregnancy of each cow. [ABSTRACT FROM AUTHOR]

The aim of the study was to determine the effect of the number and stage of lactations, time of day and calving season of cows on milk yield from a single milking, average milking time, average milking per minute, daily milking frequency and the relationship between the tested parameters of quarter milking. The study included a herd of 65 Polish Holstein Friesian black and white cows used in a free-range barn located in south-west Poland. The animals were kept in proper welfare conditions, fed using the partly mixed ration (PMR) method on the feeding table. The milk was obtained using the Lely-Astronaut A4 Automatic Milking System (AMS). The animals on the dairy cattle farm were used in the range from the first to the seventh lactation, i.e., at the age of 2.0 to approximately 10 years. In this study, the amount of milk yielded from the hind quarters was statistically significantly higher (p < 0.05) than the trait determined for the front quarters. At the same time, the milk flow rate was statistically significantly higher (p < 0.05) in the front quarters compared to the rear quarters. The daily milk yield in right rear (RR) and left rear (LR) hind quarters was higher by 1.0 kg of milk, respectively, than in right front (RF) and left front (LF) fore quarters. The milking time of the RR and LR hind quarters during the day was longer by 104.9 and 128.8 s, respectively, than the RF and LF fore quarters. The milking speed of the RR and LR hind quarters during the day was lower by 0.2 and 1.12 g/s, respectively, than in the RF and LF fore quarters. The values of the correlation between the yields of milk and its components obtained in this study were high and positive. Correlations between the milk yield and the content of its components were negative. The obtained results confirmed that the natural physiological variability of the udder and teats structure, as well as the course of lactation, significantly affects the individual composition and milk flow during milking. The ability to regulate the milk flow by adjusting the appropriate negative pressure during the robot’s operation, in the observed variability of individual lobes of the mammary gland, increases the efficiency of milking and, as a result, reduces the risk of mastitis in cows.

Were analyzed data of 229 Lithuanian Black-and-White and Lithuanian red cows. The aim of our study was to analyze milking parameters (milk yield, milking duration, milk flow measures, electrical conductivity of milk) at udder quarters level by breed. In our study the data of milk yield (kg), milking time (min), milk electrical conductivity (mS/cm), average milk flow (kg/min) and peak milk flow (kg/min) were analyzed. From Lithuanian Black-and-White cows' rear quarters were produced more milk, higher average milk flow rate was detected and therefore milking duration was shorter. Lithuanian Black-and-White cows left front quarter produced more milk but average milk flow rate fixed lower. The electrical conductivity in right side udder quarters was higher than in left side quarters in both breeds. [ABSTRACT FROM AUTHOR]