Automatic Milking System Research Articles

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.

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.

Predicting dairy herd resilience on farms with conventional milking systems.

This research paper addresses the problem that, thus far, there is no method available to predict herd resilience for farms that do not use automated milking systems (AMS). Recently, a methodology was developed to estimate both individual cow as well as herd resilience using daily milk yield observations at individual cow level from farms with AMS. This AMS-based method, however, is not suitable on farms that use conventional milking systems (CMS) where such individual cow milk yield observations are lacking. Therefore, this research aimed at predicting herd resilience using herd performance data that is commonly available on CMS farms. To do so, data consisting of 585 Dutch AMS farms where herd resilience estimates using the AMS-based method were available was examined. To predict herd resilience with herd performance data, only those data that are also commonly available on CMS farms were used in a 5-fold cross validation Random Forest model. These herd resilience estimates were subsequently compared with the AMS-based herd resilience estimates. Results showed that it is possible to predict with a 69.9% probability whether a herd performs with above or below average herd resilience using only variables available on CMS farms. Especially, the proportion of cows with an indication of rumen acidosis, proportion of cows with an elevated somatic cell count and the fluctuation in herd size over the years are good predictors of herd resilience. Since herd management decisions appear to affect herd resilience, a lower predicted herd resilience could be taken as a general indication that tactical or strategic management changes could be taken to improve the herd resilience.

Associations of cow- and herd-level factors during the dry period with indicators of udder health in early-lactation cows milked by automated milking systems

This observational study aimed to determine the association of cow-level factors and herd-level housing and management practices during the dry period with indicators of udder health in early-lactation cows in automated milking system (AMS) herds. Data were collected from 166 commercial AMS dairy farms (mean ± standard deviation = 116 ± 111 milking cows; range = 39 to 1,200) across Canada between October 2018 and September 2020. Information on herd demographics, housing, and management practices was obtained on each farm using 2 surveys. On each farm, we selected all cows that had available Dairy Herd Improvement (DHI) somatic cell count (SCC) data for their last milk test before dry-off (>250 d in milk) and their first milk test after calving (5-45 d in milk). Data from 14,007 cows were included after excluding cows with a dry period of <30 d and >120 d. Using the SCC data, we calculated for each cow the somatic cell score (SCS) for the last milk test before dry-off (PreSCS) and the first milk test after calving (PostSCS), which we then averaged per herd at a test-day level. Intramammary infection (IMI) was estimated using cow SCC data. Each cow was classified as not infected (SCC <200,000 cells/mL) or infected (SCC ≥200,000 cells/mL) at her last milk test before dry-off and her first milk test after calving. Based on this classification, cows were further categorized as never infected, always infected, new IMI, or cured IMI. At the cow level, a higher PostSCS was associated with longer dry periods. The odds of having a new IMI were higher for cows of higher parity and that had lower 305-d milk yield before dry-off. Cows with lower parity were more likely to cure an IMI. At the herd level, a higher 305-d milk yield before dry-off was associated with a lower incidence of new IMI and a higher incidence of cured IMI. Separating cows into a different pen as preparation for dry-off tended to be associated with a lower PostSCS and incidence of new IMI. At dry-off, herds that used teat sealants and blanket antibiotic dry cow therapy also had lower PostSCS. During the dry period, housing cows in different groups was associated with a higher PostSCS and a lower incidence of cured IMI, while housing cows in both pack pens and stalls compared with only pack pens was associated with a lower incidence of new IMI. Finally, placing cows onto the AMS to be milked one or more days after calving tended to be associated with a lower PostSCS compared with placing them in the AMS within the first day postpartum. In summary, indicators of udder health in early-lactation cows in AMS herds were associated with several cow-level factors and herd-level housing and management practices before dry-off, at dry-off, during the dry period, and at the beginning of lactation. Thus, if some of the associations identified are causal, AMS producers may be able to improve udder health through modifications of housing and management practices.

Exploring indicators of genetic selection using the sniffer method to reduce methane emissions from Holstein cows.

This study aimed to evaluate whether the methane (CH4) to carbon dioxide (CO2) ratio (CH4/CO2) and methane-related traits obtained by the sniffer method can be used as indicators for genetic selection of Holstein cows with lower CH4 emissions. The sniffer method was used to simultaneously measure the concentrations of CH4 and CO2 during milking in each milking box of the automatic milking system to obtain CH4/CO2. Methane-related traits, which included CH4 emissions, CH4 per energy-corrected milk, methane conversion factor (MCF), and residual CH4, were calculated. First, we investigated the impact of the model with and without body weight (BW) on the lactation stage and parity for predicting methane-related traits using a first on-farm dataset (Farm 1; 400 records for 74 Holstein cows). Second, we estimated the genetic parameters for CH4/CO2 and methane-related traits using a second on-farm dataset (Farm 2; 520 records for 182 Holstein cows). Third, we compared the repeatability and environmental effects on these traits in both farm datasets. The data from Farm 1 revealed that MCF can be reliably evaluated during the lactation stage and parity, even when BW is excluded from the model. Farm 2 data revealed low heritability and moderate repeatability for CH4/CO2 (0.12 and 0.46, respectively) and MCF (0.13 and 0.38, respectively). In addition, the estimated genetic correlation of milk yield with CH4/CO2 was low (0.07) and that with MCF was moderate (-0.53). The on-farm data indicated that CH4/CO2 and MCF could be evaluated consistently during the lactation stage and parity with moderate repeatability on both farms. This study demonstrated the on-farm applicability of the sniffer method for selecting cows with low CH4 emissions.

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.

Evaluation of the Metabolic Relationship between Cows and Calves by Monitoring Calf Health and Cow Automatic Milking System and Metabolic Parameters.

With this study, we investigated the relationship between a cow's and calf's metabolic state, and its effect on health status. To achieve this, 20 calves of primiparous and 20 calves of multiparous cows were selected. The calves were monitored for 30 days and scored for signs of disease, as described in McQuirk (2008); according to score, they were divided into healthier calves; the Low calf score group (LCS, 5-8), Medium calf score group (MCS, 9-12) and High calf score group (HCS, 14-17); or calves most prone to disease. Their mothers were monitored for the same period with a Lely Astronaut 3 herd management system (Lely, Maassluis, The Netherlands) for rumination time, milk yield, milk fat, protein, lactose concentrations and milk fat to protein ratio. Both cows and calves were sampled for blood, and concentrations of glucose with β-hydroxybutyrate were registered. The results indicate that primiparous cows had a 16% higher blood glucose concentration (3.03 mmol/L SE = 0.093) compared with multiparous cows (2.61 mmol/L, SE = 0.102) (p < 0.01), but no difference in calf glucose was recorded. Β-hydroxybutyrate levels did not differ significantly between cows and calves by parity group. Rumination time was longest in the HCS group at 550.79 min/d. and was 16% longer compared with the LCS group (461.94 min/d.; p < 0.001) and 8% longer compared with the MCS group (505.56 min/d.; p < 0.001). The MCS group rumination time mean was statistically significantly higher compared with the LCS group by 8% (p < 0.001). Milk yield was also highest in the HCS group (44.8 kg/d.): 19% higher compared with the MCS group (36.31 kg/d., p < 0.001) and 13% higher than the LCS group (38.83 kg/d., p < 0.001). There was also a significant difference between the MCS and LCS groups of 6% (p < 0.001). The HCS group had the highest milk fat concentration (4.47%): it was 4% higher compared with the LCS group (4.28%, p < 0.001) and 5% higher than the MCS group (4.25%, p < 0.001). Milk fat to protein ratio was highest in the HCS group (1.21) and was 7% higher than in the MCS group (1.12, p < 0.001) and 8% higher than in the LCS group (1.11, p < 0.001). The LCS group was determined to have the highest concentration of milk lactose (4.66%). It was 1% higher compared with the MCS group (4.62%, p < 0.001) and 1.07% higher than the HCS group (4.61%, p < 0.001). We can conclude that parity did not affect calf health status and that cows of the HCS group showed symptoms of negative energy balance expressed through higher milk yield, higher milk fat concentration and higher milk fat to protein ratio, with lower milk lactose concentration. Further and more thorough research is needed to evaluate the relationship between pregnant cows and calves.

The relationship between feed efficiency and behaviour differs between lactating Holstein and Jersey cows.

In dairy production, high feed efficiency (FE) is important to reduce feed costs and negative impacts of milk production on the climate and environment, yet little is known about the relationship between FE, eating behaviour and activity. This research communication describes how cows differing in FE, expressed as daily energy corrected milk production per unit of feed intake, differed in eating behaviour and activity. We used data from a study of 253 lactations obtained from 97 Holstein and 91 Jersey cows milked in an automatic milking system. Automated feed troughs recorded feed intake behaviour and cows wore a sensor that recorded activity from 5 to 200 d in milk (DIM). We used a mixed linear model to estimate random solutions for individual cows for traits of steps, lying and eating behaviour and calculated their correlation with FE during four periods (5-35, 36-75, 76-120 and 121-200 DIM). Separate analyses were performed for each breed and period. We found that individual level correlations between FE and behaviour traits were stronger in Jersey than in Holstein cows. Eating rate correlated weakly negatively to FE in Holstein cows and more strongly so in Jersey cows, such that efficient Jerseys were slower eaters. The physical activity of Jersey cows was weakly and negatively correlated to FE, but this was not the case in Holstein cows. We conclude that eating rate was consistently negatively associated with FE throughout lactation for Jersey cows, but not for Holstein cows.

Effect of pre- and postpartum supplementation of a pure glycerol product to dairy cows on feed intake, metabolic markers, and milk yield and components

The objective of this study was to quantify the effects of supplementing transition dairy cows with a low inclusion dry glycerol product in the pre- and postpartum periods on feed intake, metabolic markers, and milk yield and components. Multiparous Holstein dairy cows (n = 60) were enrolled in a 2-by-2 factorial design study. Starting 21 d before expected parturition, cows individually received a dry cow diet with (1) 250 g/d glycerol product supplementation [66% pure glycerol (United States Pharmacopeia grade); GLY], or (2) no supplementation (CON) mixed to their total mixed ration. After parturition, cows, again, were individually assigned to either GLY, or (2) no supplementation (CON) to their partial mixed ration for the first 21 d in milk (DIM). Cows were milked by an automated milking system and offered a target of 5.4 kg DM/d pellet (23% of target total dry matter intake, DMI) in the automated milking system and followed for 42 d into lactation. Blood samples were collected 6.3 ± 3.47 d before calving for all blood measures and 3, 7, 10, and 14 DIM for analysis of glucose and β-hydroxybutyrate, as well as 3 and 7 DIM for nonesterified fatty acids (NEFA) and haptoglobin. Initial dry cow body weight (BW), calf birth weight, previous 305-d milk, and month of parturition were used as covariates in the statistical model. Cows supplemented with GLY prepartum lost less BW and consumed more DMI pre- and postpartum, as well as had lower postpartum blood β-hydroxybutyrate and NEFA concentrations compared with those fed the CON treatment prepartum. Cows supplemented with GLY postpartum had lesser DMI in the first 42 DIM than cows fed CON postpartum, but also had reduced blood NEFA concentrations, odds of a high haptoglobin test, odds of a low blood glucose test, and lesser preformed fatty acid concentrations and yields in their milk. Cows supplemented glycerol both pre- and postpartum lost the least total BW from -21 to 21 DIM. No treatment effects were detected for milk yield; however, cows receiving GLY postpartum had lower milk fat. Overall, glycerol supplementation during the transition period, particularly during the 21 d before calving, was associated with markers of improved metabolic status.

Antimicrobial and teat sealant use and selection criteria at dry-off on Canadian dairy farms

Infections with antimicrobial resistant pathogens are a major threat to human and animal health worldwide. Further, reduction of livestock-associated antimicrobial use (AMU) is often identified as an area of focus. Selective dry cow therapy (DCT) warrants consideration as an important way to decrease AMU on Canadian dairy farms. In addition, teat sealants (TS) are a nonantimicrobial alternative for prevention of intramammary infection during the dry period. Therefore, objectives of this study were to determine how antimicrobials and TS are used at dry-off on Canadian dairy farms to determine selective DCT uptake and enacted selection protocols. It was expected that these data will provide a baseline understanding of DCT practices and highlight areas for future intervention to further reduce AMU. An observational study was conducted utilizing 2 in-person questionnaires conducted between July 2019 and September 2021 on 144 participating dairy farms in 5 Canadian provinces (British Columbia = 30, Alberta = 30, Ontario = 31, Québec = 29, and Nova Scotia = 24). Overall, 45 farms (31%) reported adopting selective DCT, 95 (66%) enacted blanket DCT, and 4 (3%) did not provide antimicrobial DCT. Farms enacting selective DCT had approximately 50% less intramammary antimicrobials used at dry-off compared with blanket DCT farms. Cow somatic cell count history was the most common criterion for selective DCT decision-making, followed by previous clinical mastitis history, bacteriological culture, and milk production. A slight majority of farms (56%) applied TS to all cows at dry-off, whereas 17 farms (12%) used TS selectively, and 46 farms (32%) did not use TS. Larger herds more often used TS, and farms with an automatic milking system more often used TS selectively than applied to all cows. Results highlighted the variability in antimicrobial treatment and TS use protocols at drying off on Canadian dairy farms, and the potential for further antimicrobial reduction with increased adoption of selective DCT.

What have we learned about milk quality and udder health in automatic milking systems?

The adoption of automatic milking systems (AMS) continues to increase across North America. An analysis of milk quality data from more than 3,000 dairy farms in Ontario, Canada sug­gests that while AMS herds have a slightly higher average raw bacteria count and higher risk of incurring a bacterial or freez­ing point penalty, the bulk tank somatic cell count is similar among farms using pipeline, parlor and AMS systems. These results indicate that farms milking with AMS can produce milk of similarly high quality as those milking with conventional milking systems (CMS). A panel of individuals shared their opinions about advantages and disadvantages of AMS and CMS, with a number of AMS attributes appearing in both lists, sug­gesting that the perception will depend on the context and the attributes of the AMS.

Association between personality traits of dairy cows and their peripubertal heifer offspring

This study aimed to determine if personality traits identified in dairy cows during the transition phase are correlated with those of their peripubertal heifer calves. At ~24 d before calving and ~24 d after being first introduced to the automated milking system, the personality traits of 23 Holstein cows were assessed using a combined arena test [consisting of exposure to consecutive novel environment (NE), novel object (NO), and novel human (NH) tests]. Personality traits were established by principal component analysis (PCA) of behaviors expressed in these tests. The PCA of cow behaviors during the precalving test revealed 3 factors, or personality traits, interpreted as exploratory, active, and bold. The PCA of cow behaviors during the postcalving test revealed 2 factors, interpreted as active and exploratory. From these cows, 23 female Holstein heifers were produced and enrolled in this study at 7 mo of age. Heifers were personality tested once through a similar combined arena test. The PCA of heifer behaviors during the NO test revealed 3 factors, interpreted as bold, exploratory-active, and social. The PCA of heifer behaviors during the NH test revealed 2 factors, interpreted as exploratory-active and social. All factor scores from each cow and heifer pair were tested for association. An association between cows with higher scores on the factor interpreted as exploratory and heifers with lower scores on the factor interpreted as bold was detected. There were tendencies for cows with higher scores on the factor interpreted as active to be associated with heifers that scored highly on the factors interpreted as exploratory-active and bold. The data suggests that there are some limited associations between personality traits of cows and their heifer offspring; further exploration of these associations may lead to the prediction of heifer personality based on cow personality.

Replacing silage with large amounts of concentrate and straw affects milk production, economics and climate differently in Holstein and Jersey Cows

With droughts becoming more frequent due to climate changes alternative feeding strategies are required. The aim of this study was to examine the effects of replacing all or part of the silage component with barley straw and concentrates for Holstein and Jersey cows. Forty-nine Holstein and 43 Jersey cows were fed either the C49 diet (Roughage-to-concentrate ratio [% of DM; dry matter] of 51:49), a diet where concentrate constituted 70% of DM (i.e., diet C70; 30:70), or the extreme diet (i.e., C91; 9:91), where 91% of DM was concentrate and 9% of DM was barley straw. Cows were allowed up to 2.65 kg DM per day from concentrate in the automatic milking system (AMS). Cows were fed their diets for 5 weeks. All diets were formulated to provide the same daily net energy intake. Daily feed intake and milk production responses were recorded. Volatile fatty acid profiles in rumen fluid were measured for Holstein cows. Impact on economy and carbon footprint were calculated. Data were analyzed using a general linear mixed model. For both breeds, total dry matter intake (i.e., AMS concentrate + partially mixed ration) decreased with increasing concentrate proportion. In neither Holstein nor Jersey, experimental diet affected milk yield. A decrease in fat percentage and ECM was observed for both breeds, when the level of concentrate was increased. However, there was an interaction between diet and breed for fat percentage and for energy-corrected milk yield (ECM), and there was an interaction for feed efficiency expressed as kg of ECM/kg of DM intake. For Holstein cows, acetate molar proportion in rumen liquid decreased and propionate molar proportion increased when silage was reduced. Regardless of breed, increasing concentrate in the ration did not affect carbon footprint per kg ECM, but caused a decrease per cow per day, and per kg DM intake. However, if the contribution from direct land use changes and soil C were taken into account, increasing concentrate in the ration reduced carbon footprint per kg ECM due to decreased proportion of soybean meal in C75 and C91 diets. Production of milk from predominantly concentrate based diets containing no grass silage reduced income over feed cost for both breeds but more for Holsteins. In conclusion, results showed that it is possible to keep dairy herds and produce milk even during extreme drought, when availability of silage is minimal, whereas such ration decreased income over feed cost.

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.

Precision Livestock Farming Research: A Global Scientometric Review.

Precision livestock farming (PLF) utilises information technology to continuously monitor and manage livestock in real-time, which can improve individual animal health, welfare, productivity and the environmental impact of animal husbandry, contributing to the economic, social and environmental sustainability of livestock farming. PLF has emerged as a pivotal area of multidisciplinary interest. In order to clarify the knowledge evolution and hotspot replacement of PLF research, based on the relevant data from the Web of Science database from 1973 to 2023, this study analyzed the main characteristics, research cores and hot topics of PLF research via CiteSpace. The results point to a significant increase in studies on PLF, with countries having advanced livestock farming systems in Europe and America publishing frequently and collaborating closely across borders. Universities in various countries have been leading the research, with Daniel Berckmans serving as the academic leader. Research primarily focuses on animal science, veterinary science, computer science, agricultural engineering, and environmental science. Current research hotspots center around precision dairy and cattle technology, intelligent systems, and animal behavior, with deep learning, accelerometer, automatic milking systems, lameness, estrus detection, and electronic identification being the main research directions, and deep learning and machine learning represent the forefront of current research. Research hot topics mainly include social science in PLF, the environmental impact of PLF, information technology in PLF, and animal welfare in PLF. Future research in PLF should prioritize inter-institutional and inter-scholar communication and cooperation, integration of multidisciplinary and multimethod research approaches, and utilization of deep learning and machine learning. Furthermore, social science issues should be given due attention in PLF, and the integration of intelligent technologies in animal management should be strengthened, with a focus on animal welfare and the environmental impact of animal husbandry, to promote its sustainable development.

Multi-algorithm clustering analysis for characterizing cow productivity on automatic milking systems over lactation periods

The introduction of Automated Milking Systems (AMSs), or milking robots, represented a significant advancement in dairy farming techniques. AMSs enable real-time monitoring of udder health and milk quality during each milking episode, which provides a wealth of data that can be utilized to optimize herd management practices. ML algorithms are well-suited for handling large and multi-dimensional datasets, making them a valuable tool for analyzing the vast amount of data generated by AMSs. This study introduces a novel approach to characterize the milk productivity of Holstein Friesians cows milked by AMSs during individual lactation periods and evaluate their stability over time. Four unsupervised ML clustering algorithms were employed to cluster the cows within each lactation period, and a merging index was proposed to combine the clustering results. The dairy cows were grouped into clusters based on their productivity, and the stability of these Productivity Groups (PGs) over time was analyzed. The PGs were found to be weakly stable over time, indicating that selecting cows for insemination based solely on their present or past lactation productivity may not be the most effective strategy. In addition, the results revealed that the High Productivity Group exhibited lower levels of protein, fat, and lactose content in the milk. The proposed methodology was demonstrated using data from one farm with dairy cows that exclusively uses the AMS, however, it can be applied to any context and dataset in which a multi-algorithm clustering analysis is suitable, including data from conventional milking parlors. Understanding milk productivity and its factors in future lactation periods is essential for effective herd management. A comprehensive long-term analysis is of significant importance for the zootechnical sector as it could assists farmers in selecting cows for insemination and making decisions on which ones to retain for future lactation periods.

Design of a Teat Cup Attachment Robot for Automatic Milking Systems

Automatic milking systems (AMSs) for medium and large dairy farms in China require manual assistance to attach the teat cup, which greatly affects the milking efficiency and labor costs. In this regard, it is necessary to realize the automatic completion of cow teat attachment work. To address this issue, the authors developed a teat cup attachment robot for an AMS based on the theory of the solution of inventive problems (TRIZ). Specifically, we developed an enhanced algorithm for teat detection and designed a six-degree-of-freedom manipulator with integrated drive control. The design parameters were simulated and analyzed to validate their efficacy, while the rationality of the manipulator’s movement during teat cup attachment was verified. The maximum displacement and angle error of the cup was 1.625 mm and 1.216 mm, respectively, as verified by the teat cup attachment error test. A dynamic response test showed that the manipulator could follow the teat of the cow in real time. The attachment time for teat cups was 21 s per cow, with a success rate of 98%. The performance of the teat cup attachment robot was capable of meeting the automatic attachment teat cup needs for medium and large dairy farms during milking.

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.

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.

149 Case Study: The Impact of a Fogging System on Dairy Cow Comfort in Cows Housed in a Barn with Tunnel Ventilation and an Automatic Milking System

Abstract Fogging systems control microclimatic parameters of barns to provide immediate cooling to dairy cows during heat stress conditions. However, the benefits of implementing a fogging system to cow comfort behaviors have not been thoroughly investigated in lactating cows. Therefore, the objective of this case study was to evaluate cow comfort behaviors and milk productivity in cows managed in a tunnel-ventilated barn in a commercial dairy farm that uses an automatic milk system. Sixty Holstein lactating dairy cows (45-90 days in milk; DIM) were monitored and housed in group pens (6 pens; n = 10 cows/pen). Four weeks before the fogging system installation, cows had pedometers (IceQube, IceRobotics, Inc., United Kingdom) and SCR rumination collars (SCR by Allflex; Israel) placed for continuous daily activity and rumination levels monitoring, respectively. Productivity information was acquired using automatic robotic milking technology (Lely Astronaut A5; Iowa, United States), and all variables were measured until four weeks after fogger system installation. Activity behavior (lying duration, daily step count, and number of transitions) and productivity (daily milk yield and milk speed) were evaluated before (PRE; 20-day pre-installation period), during (DURING; 2-day period), and after fogger installation (POST; 35-day post-installation period). Data were analyzed using PROC GLM in SAS 9.4 with cow as the experimental unit and temperature-humidity index (THI) included in the model as a covariate. Overall, cows spent more time lying (P &amp;lt; 0.01) in the POST period versus PRE and DURING. Cows performed fewer daily transitions between lying and standing (P &amp;lt; 0.01) in the POST versus PRE period, but no differences were detected (P = 0.67) among the step counts observed PRE, DURING, and POST fogger installation. Average milk speed and average milk yield were decreased (P &amp;lt; 0.01) in the POST period compared with the PRE and DURING periods. Temperature-humidity index significantly influenced (P &amp;lt; 0.01) lying time but did not have an effect on any other variables measured. In summary, installation of a fogger system in a commercial tunnel-ventilated barn improved cow comfort behaviors in lactating cows managed with an automated robotic milking system but did not offer immediate production performance benefits.