Robotic Milking Systems Research Articles

On Developing a Machine Learning-Based Approach for the Automatic Characterization of Behavioral Phenotypes for Dairy Cows Relevant to Thermotolerance

The United States is predicted to experience an annual decline in milk production due to heat stress of 1.4 and 1.9 kg/day by the 2050s and 2080s, with economic losses of USD 1.7 billion and USD 2.2 billion, respectively, despite current cooling efforts implemented by the dairy industry. The ability of cattle to withstand heat (i.e., thermotolerance) can be influenced by physiological and behavioral factors, even though the factors contributing to thermoregulation are heritable, and cows vary in their behavioral repertoire. The current methods to gauge cow behaviors are lacking in precision and scalability. This paper presents an approach leveraging various machine learning (ML) (e.g., CNN and YOLOv8) and computer vision (e.g., Video Processing and Annotation) techniques aimed at quantifying key behavioral indicators, specifically drinking frequency and brush use- behaviors. These behaviors, while challenging to quantify using traditional methods, offer profound insights into the autonomic nervous system function and an individual cow’s coping mechanisms under heat stress. The developed approach provides an opportunity to quantify these difficult-to-measure drinking and brush use behaviors of dairy cows milked in a robotic milking system. This approach will open up a better opportunity for ranchers to make informed decisions that could mitigate the adverse effects of heat stress. It will also expedite data collection regarding dairy cow behavioral phenotypes. Finally, the developed system is evaluated using different performance metrics, including classification accuracy. It is found that the YoloV8 and CNN models achieved a classification accuracy of 93% and 96% for object detection and classification, respectively.

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

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

Impact of different stall layouts with robotic milking systems on the behavioral pattern of multiparous cows

The present study aimed to compare the efficiency of different pens and animal flow configuration layouts in freestall pens using a robotic milking system (RMS) with guided flow based on the behavioral patterns of multiparous lactating Holstein dairy cows in a commercial farm. The behavior of 24 cows in freestall pens was evaluated, divided into 4 different stall configurations: original (OR), conversion (CVS), toll-booth I (TBI), and toll-booth II (TBII), each featuring distinct circulation layouts with different configurations of location, position, and number of guided-flow RMS equipment, feed bunk, water trough, commitment pen, sand beds, sorting gates, and one-way gate. Six multiparous cows, parity 2 or 3, with an average (± SD) of 180 ± 20 DIM, were randomly selected from each freestall pen for focal assessment of behavioral patterns. The location, position, and behavior of each animal were recorded in a field ethogram, with individual sequences recorded at 15-min intervals using the focal method during 6 nonconsecutive 10-h periods in each pen, and the proportion of observed time for each behavior was assessed. The pens differed in the number of available milking robots (1, 2, or 3), the number of animals per robot, the quantity and orientation of smart doors, and the placement of feed bunks, water troughs, and sand beds. A completely randomized experimental design was used to compare the 4 stalls, with a nonparametric Kruskal-Wallis test, in which the medians of the treatments were then compared with the Dunn test at a significance level of 5%, using the Minitab software. The behavioral pattern of cows exhibited differences based on the stall configuration with RMS as a proportion of the observed time (60 h). The TBI stall configuration, where the animal needs to be milked to exit the milking robot, showed a higher percentage of observed time spent in the sand bed (68%) and lying position (64%) compared with other stalls, as a proportion of the observed time (60 h). Notably, the TBII stall exhibited a significant amount as a proportion of observed time (60 h) in the holding area (16%), possibly attributed to a layout with a higher number of animals per robot, emphasizing the importance of respecting the number of animals per robot when housing in a stall with RMS. Thus, spatial configuration and the density of robotic systems are factors that influence the behavioral pattern of dairy cows.

Comparative Analysis of Milking and Behavior Characteristics of Multiparous and Primiparous Cows in Robotic Systems.

Robotic milking systems are successful innovations in the development of dairy cattle. The objective of this study was to analyse the milking characteristics and behavior of dairy cows of different calving orders in "milk first" robotic milking systems. The data were collected from a commercial herd located in the Midwest region of Minas Gerais (Brazil), which uses an automatic milking system (AMS TM, DeLaval). Were analysed 26,574 observations of 235 Holstein cows were available. Data were evaluated by multivariate analysis of variance and the Tukey test. - Tthe characteristics milk flow and milking efficiency were more favourable for multiparous cows (p <0.01), while the time in the stall was more favourable for primiparous females (p <0.01). The values of handling time were better in the primiparous cows (p <0.01). Primiparous cows had higher amounts of kick-off (p <0.001), and multiparous cows had higher incomplete milkings (p <0.001). The number of incomplete milkings showed a higher ratio in terms of reduction in milk production in 26.6% in primiparous cows and 26.7% in multiparous cows (p <0.01). Regarding the behavioral characteristics, primiparous cows had higher amounts of kickbacks, while multiparous cows had greater quantities of incomplete milkings.

ЭТАПЫ РАЗРАБОТКИ ОТЕЧЕСТВЕННОГО ПРОГРАММНО-АППАРАТНОГО КОМПЛЕКСА РОБОТИЗИРОВАННОЙ СИСТЕМЫ ДОЕНИЯ

The Republic of Belarus has the potential to milk production increasing till 9.7-9.8 mln ton by 2025. This task’s fulfillment by a set of organizational and technological measures completing the agricultural and other organizations’ phased specialization and it transitions to industrial, intensive milk production technologies carrying out can be achieved. This research’s results conducted on Republic of Belarus dairy farms indicates the robotic milking machines’ need for import substitution and domestic robotic milking equipment development, the creation of which is proposed in several stages to be carried out. At the initial stage, it is necessary a software-and-apparatus complex and robotic milking system (positioning system of robotic executive bodies) actuators, that is the robotic milking units’ central link developing. At each of the subsequent stages, the robotic executive bodies’ positioning system will be retrofitted with a multiparametric milk quality control system, that will allow to respond changes promptly in case of their occurrence and recognize acidosis and ketosis at an early stage. An even higher equipment level will ensure the robotic milking machines in combination with automated control systems using, that will make it possible the milking process, control the milk quality optimizing, as well as dispensed concentrate feed amount normalizing. At robotic milking systems developing, it is necessary to take the simplest, universal solutions that can be applied within any equipment’s configuration with an unification’s high level (including the separate units for different types’ existing milking machines upgrading using), interchangeability, maintainability. So as the robotic systems using in the Republic it is implemented everywhere, our own simple and original solutions’ development will provide a manufactured equipment’s much higher level in compared to copying finished products, involving the expensive elements, devices and entire units using.

JUSTIFICATION OF THE BASIC PARAMETERS OF A DAIRY FARM FOR 500 COWS WITH ROBOTIC MILKING SYSTEMS

The article highlights the results of research on the assessment of the conditions for keeping high-yielding cows in a new type of room with a width of 36 m and a height of 10.5 m according to indicators of the microclimate and daily behavior of cows. It has been established that the volume-planning and technological solutions of this room with robotic milking systems provide comfortable conditions for keeping highly productive cows. So, the air temperature and relative humidity of the room meet the existing standards. The presence of side curtains and a light-aeration comb ensure a high speed of air movement at the level of 0.65 m/s, which has a positive effect on its gassiness. The ammonia content is at the level of 2.9 mg/m3 against 20 mg/m3 according to regulations, and the level of hydrogen sulfide is 0.2 mg/m3 against 10.0 mg/m3, and the level of carbon dioxide is two times lower. Studying the behavior of cows under the conditions of a new type of room, it has been established that animals show the greatest activity during the change in daylight hours, and the least one - in the middle of the day or night. The behavior of cows under the conditions of the new technology changes depends on the productivity of the cows. Thus, as productivity increases, the duration of feed and water consumption and stay in the robotic box increases. Animals with the productivity of 20 kg stand for 13 hours and lie down for 11 hours, while cows with the productivity of 40 kg stand for only 10 hours and lie for 14 hours, which corresponds to their physiological needs. Based on the obtained research results, we have justified the main parameters of a new type of premises with robotic milking systems for 500 cows.

ТЕХНИКО-ТЕХНОЛОГИЧЕСКАЯ ОЦЕНКА ДОИЛЬНЫХ РОБОТОВ РАЗЛИЧНЫХ ПРОИЗВОДИТЕЛЕЙ

Now one of the urgent problems in dairy cattle breeding is the shortage of workers and specialists. The solution to the problem is the introduction of a voluntary robotic milking system. (Research purpose) The research purpose is conducting a comparative analysis of milking robots of the most popular manufacturers in the world, such as Lely, DeLaval, Gea Farm Technologies. (Materials and methods) Studies were carried out on three farms equipped with milking robots of the studied companies. We used our own measurements, as well as data from the herd management programs of milking robots. (Results and discussion) It was determined that in the first 3 months of lactation, with free milking on milking robots, it is possible to get up to 15 percent more milk than with double milking on the Herringbone milking machine. It was found that the Lely Astronaut A4 milking robot has the highest number of milked cows - 68 heads on a one-box and 135 on a two-box, DeLaval VMS have a large tilt of the milking cups when connected to the udder nipples (45 degrees). It was shown that Lely Astronaut A4 surpasses its competitors in total milking time (6.03 minutes), consumes 36 percent less water than VMS and 35 percent less than MIone, and consumes 42 percent less electricity than VMS and 52.7 percent less than MIone. It was found that after brushing the hardened contaminated udder teat with the Astronaut A4 milking robot, bacterial contamination on the surface of the nipples averaged 470 CFU in 1 cubic centimeter, whereas after washing the teats with VMS and MIone milking robots, bacterial contamination amounted to 45.0 thousand CFU in 1 cubic centimeter. (Conclusions) It was stated that Lely Astronaut A4 milking robots surpass their competitors in many aspects, such as milking speed, udder teat cleaning quality, as well as electricity and water consumption.

Application of multiblock analysis to identify key areas and risk factors for dairy cow persistence

The present study analysed the importance of individual variables and different thematic blocks of production areas, management, and herd infectious disease status on cow persistence, characterised by herd on-farm mortality rate (MR), culling rate (CR), and mean age of culled cows (MAofCC) applying multiblock partial least squares (mbPLS) analysis. This study included 120 free-stall dairy herds with ≥ 100 cows. Data on the previous year’s predominant cow housing system and management practices were collected, and on-farm measurements and cow scoring were performed. Bulk tank milk (BTM) and heifer blood samples (10 samples per herd) were collected and analysed for antibodies against the selected pathogens. In total, 172 variables were aggregated into 14 thematic blocks. The annual CR, MR, and MAofCC values were calculated for each herd. Thematic blocks with significant impact on cow persistence (included herd MR, CR and MAofCC) were ‘infectious diseases’ (block importance index out of all blocks = 13.6%, 95% CI 10.3; 20.5), ‘fertility management’ (16.3%, 95% CI 6.8; 26.9), ‘lactating cow management’ (11.5%, 95% CI 6.4; 17.8), ‘milking’ (11.3%, 95% CI 3.2; 17.1), ‘herd characteristics’ (10.1%, 95% CI 6.3; 14.2), ‘close-up period management’ (9.7%, 95% CI 2.7; 15.7), ‘calving management’ (7.9%, 95% CI 3.1; 11.4) and ‘disease management’ (7.3%, 95% CI 0.2; 12.0). Variable categories with the highest importance in explaining composite outcome including herd MR, CR and MAofCC were rear-end and udder lesions in ≥ 20% of the cows, BTM and heifers seropositive to bovine respiratory syncytial virus, vaccination against bovine herpesvirus 1, twice daily milking and herd location in Northwest region. Larger herd size, higher levels of milk yield, and rearing predominantly Holstein breed cattle were herd factors associated with poorer cow persistency. Grazing cows and having semi-insulated barns were associated with lower CR and MR, respectively. Heat detection and farm pregnancy testing strategies were significant factors in the fertility block. Using disposable dry papers for teat cleaning and not using any wet teat-cleaning tools were risk factors for high MR. A robotic milking system was protective for increased herd MR and CR. A high pre-calving body condition score and poor rear body cleanliness of ≥ 30% of cows were associated with inferior herd persistency outcomes. Calving in group pens with deep litter bedding was associated with a lower CR. Multiblock PLS model is innovative tool that helped to identify most influential farming areas but also single risk factors associated with cow persistency described by multiple parameters.

MILK PRODUCTIVITY AND REPRODUCTIVE CAPACITY OF COWS DEPENDS ON DIFFERENT MILK PRODUCTION TECHNOLOGIES

MILK PRODUCTIVITY AND REPRODUCTIVE CAPACITY OF COWS DEPENDS ON DIFFERENT MILK PRODUCTION TECHNOLOGIES

Characteristics of the sanitary quality of milk in sick cows with purulent inflammation of the uterus when using the GEA Dairy ProQ robotic milking system

Purpose : to establish the role of reproductive pathologies in high-yielding cows in reducing the sanitary quality of milk at dairy enterprises using various milking systems. Materials and methods . The studies were carried out in high-tech dairy enterprises using GEA Dairy ProQ and Ley Astronaut robotic milking systems with cow productivity of more than 11 tons of milk per lactation. 4659 lactating cows were under observation. The selection of samples of uterine secretions, samples from the udder teats, the surface of the anogenital area, mammary gland and milk for bacteriological studies was carried out sterile, then they were sown on nutrient media. Results . The contents of the uterus with pyometra in 80% are contaminated with microorganisms: S. dysgalactiae – 29.1%, S. aureus – 26.4%, E. coli – 24.2%, and S. agalactiae – 20.3%. In 12% of cows, the microflora was isolated in monoculture. In samples of milk obtained from sick cows with uterine pathology using Ley Astronaut robotic milking, it showed 1.72 times lower sanitary safety, in comparison with samples obtained from clinically healthy animals and using the GEA Dairy ProQ robotic milking system. Conclusion . In samples of milk obtained from sick cows using the Ley Astronaut robotic milking system, a microbiological study found a decrease in the sanitary quality of milk, which is an obstacle in the production of cheese.

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.

Dairy robotic milking system

The article presents the results of studying the technology of robotic milking in the aspect of digitalization of dairy cattle breeding. The relevance of the topic lies in the fact that in Russia the task is to introduce the technology of “precise” animal husbandry based on digital technologies. It is noted that in our country there are successfully functioning dairy complexes, where digital technology tools, including robotics, have been introduced. The technologies for identifying animals, determining the quality of milk, controlling the robotic complex and the milking process are described. The robotic milking system is a progressive technology in terms of the use of digital technology elements: big data, the Internet of things, artificial intelligence. It is necessary to take into account the experience of these farms in order to increase the efficiency of introducing innovations.

Efficiency of the data generated by the robotic milking system for comprehensive diagnosis of mastitis in cows

Efficiency of the data generated by the robotic milking system for comprehensive diagnosis of mastitis in cows

EFFICIENCY OF DIFFERENT MILKING SYSTEMS USAGE UNDER CONDITIONS OF RESOURCE-SAVING TECHNOLOGIES OF MILK PRODUCTION

The article presents the efficiency evaluating results of easily build cowsheds in modern milk production technologies and the use of high-productive milking plants such as "Parallel" and "Carrousel" with 32 machines each and robotic milking systems. It has been established that the new type of premises not only provides comfortable conditions for keeping high-yielding cows, but also reduces labor costs for their maintenance and, most importantly, allows the use of modern high-productive milking plants such as "Parallel" and "Carrousel", as well as the usage of “motivational milking” technology". It has been established that the technology of preparing cows for milking and milking technology provides more complete realization of milk ejection reflex at the installation of the "Parallel" type and robotic milking system. The average intensity of cows’ milk flows at these plants is 30% and 47% higher compared to those of the installation of “Carrousel” type, which is confirmed by the intensity of milk flow production at the first minute of milking.&#x0D; The multiplicity of cows’ milking of different lactations under conditions of "motivational milking" depending on their productivity and lactation period was also studied. It has been proved that the higher the daily yield, the more often the cow goes to milking. Thus, animals with a daily milk yield of 10 to 20 kg go to milking in average of 2.34 times a day, and with a daily milk yield of 40 and above - 3.39 times.&#x0D; It has been deemed that milk obtained with the help of a milking parlor of the "Parallel" type and a robotic milking system has higher values of mass fraction of fat and protein, which is associated with the genetic potential of animals, their diet and quality of milking. According to physical, chemical and technological properties, milk obtained at the milking parlors "Parallel" and "Carrousel", as well as robotic milking systems meets current standards. Due to bacterial contamination, reductase test and milk clot characteristics, milk obtained with a Parallel milking parlor as well as the robotic milking system has higher quality indicators than with the use of the Carrousel type installation. But according to a set of indicators, the milk obtained by the studied milking systems belongs to the desired class of cheese.

CLINICAL EVALUATION OF THE SUBSTANCE «GALF127KELMU1M2P» BASED ON POLYMER MATRICES WHEN USING THE GEA DairyProQ ROBOTIC MILKING SYSTEM

The research was carried out at the high-tech dairy enterprise "Russia" using the GEA DairyProQ robotic milking system of the Donskoye joint Venture of the Volgograd region. Experiments to study the effect of the substance "GALF127KELMU1M2P" have shown that the developed substance, according to the degree of exposure to the body of warm-blooded animals, belongs to low-hazard substances (hazard class 4 according to GOST 12.1.007-76). It has been proven that nipple treatment reduces the number of bacteria on the nipples and the surface of the breast, improves the sanitary quality of milk. The concentration of Staph. aigees, Strep. agalactiae, was lower when the udder nipples and breast surfaces were treated with the substance "GALF127KELMU1M2P" before and after milking, compared with untreated nipples and breast. Based on the results of the experiment, it can be concluded that the controlled treatment of the udder nipples and the breast surface with the substance "GALF127KELMU1M2P" before and after milking cows with high milk yields in 2.4% of animals does not prevent hyperkeratosis of the udder nipples. In addition, 12.1% are diagnosed with subclinical mastitis. As for the animals during disinfection of the udder nipples with Teasfoam Supercow, here hyperkeratosis of the udder nipples was noted in 3.3% of cows. Another 12.9% had sub-clinical mastitis. The results were as follows: hyperkeratosis of the udder nipples was diagnosed in 5.4% of cows. Another 26.5% of the animals became ill with subclinical mastitis. In cows of the control group, animals that did not use the drugs, inflammation of the skin of the udder nipples was noted on 34 nipples (30.4%) and inflammation of the mammary gland in 14 (12.5%) cows. The milk samples obtained from the treated nipples and breast during milking have no foreign smell, color and taste. There are no qualitative changes in milk samples obtained from cows whose nipples and mammary glands were treated during milking, so it can be concluded that the use of hygiene and disinfection of the mammary gland before and after milking cows does not change the sanitary and biochemical qualities of milk, as well as its technological properties.

Evaluating automated infrared thermography and vulva exposure tracking as components of an estrus detection platform in a commercial dairy herd

The primary objective of this study was to develop an automated infrared thermography platform (Estrus BenchMark) capable of measuring skin temperature and tail movements as a means of identifying cows in estrus. The secondary objective was to evaluate the accuracy of Estrus BenchMark to detect estrus compared to in-line milk progesterone (P4) analysis (Herd Navigator System) in a commercial dairy herd managed under a robotic milking system. Data were collected on forty-six cows from 45 to 120 d after calving. Cows were flagged in estrus when milk P4 fell below 5 ng/mL. The Estrus BenchMark true positive estrus alerts (Sensitivity; Se%) were compared to Herd Navigator System estrus alerts at different time-windows (±12 h, ±24 h, ±48 h, and ±72 h) relative to the Estrus BenchMark estrus alerts for all the estrus alerts (AE) and confidence-quality estrus (CQE; >80% quality) alerts identified by Herd Navigator System. The Estrus BenchMark captured skin temperature and tail movements resulting in vulva exposure (left tail movements, LTail; right tail movements, RTail; and pooled tail movements, PTail) for each milking event. Skin temperature tended to increase when the milk P4 concentration (Least-Squares Means ± SE) dropped for AE (estrus day [d 0]; P4; 3.51 ± 0.05 ng/mL, Skin temperature; 33.31 ± 2.38 °C) compared with d −7 (P4; 20.22 ± 0.73 ng/mL; Skin temperature: 32.05 ± 3.77 °C). The increase in skin temperature, however, was significant in cows with CQE > 80% at d 0 (32.75 ± 0.29 °C) compared to d −7 (31.80 ± 0.28 °C). The prevalence of tail movements to expose vulva was greater (P = 0.01) in AE at d 0 (LTail: 62.50%; PTail; 68.75%; and RTail: 56.25%) compared with d −7 (LTail: 18.75%; PTail: 9.37%: and RTail: 9.37%), and d +4 (LTail: 9.37%; PTail: 9.37%; and RTail: 12.5%). Moreover, the higher prevalence of tail movements at d 0 was observed in cows with CQE > 80% (LTail; 65%, PTail; 80%, and RTail; 70%) compared to those with CQE < 80%. The highest Estrus BenchMark Youden index (YJ; 0.45), diagnostic odds ratio (DOR; 9.04), and Efficiency (0.77) were achieved for AE in a ±48 h window and at ±72 h window for CQE (YJ; 0.66, DOR; 25.29, and Efficiency 0.76) relative to Herd Navigator System estrus alerts. The highest Estrus BenchMark resulted in 58% estrus detection rates for AE and 80% for cows with CQE compared to the Herd Navigator System.

Milking time behavior of dairy cows in a free-flow automated milking system

The objective of this preliminary observational study was to determine milking time behavior of cows in a free-flow automated (robotic) milking system (AMS) and identify potential factors that influenced the time waiting to be milked. Milking time behavior of 40 cows from 1 pen on a commercial dairy farm with a free-flow AMS was evaluated using video analysis over 2 d. For each study cow, data were assessed for waiting time to access the milking robot, the use of the fetch pen, robot refusals, and their lying behavior. On average, cows visited the robot to wait to be milked 6 times per day, for 15 min per visit, for a total daily waiting time of 88 min per cow (range 5 to 322 min). Daily waiting time was longer for primiparous cows and decreased with increasing days in milk, but this effect interacted with parity. Daily waiting time and number of visits to the robot were associated with voluntary use of the fetch pen. Furthermore, cows with long daily waiting times had shorter daily lying times compared with cows with short daily waiting times (9.5 vs. 11.1 h/d). It is possible that factors related to the design and layout of the AMS entry and fetch pen had an effect on waiting behavior. We inferred that adoption of grouping strategies intended to reduce competitive behavior, especially toward primiparous cows, could improve milking time behavior in a free-flow AMS. This preliminary observational data from a single herd highlights the need to confirm the findings across multiple AMS herds, both with free-flow and guided-flow systems.

The Effect of Robotic Milking Systems on Economic Performance of Dairy Farms with a Simulation Model

The most remarkable technology brought to dairy farms by the digital transformation in agriculture is undoubtedly robotic milking systems (RMS). Knowing the economic impact of this technology is essential for farmers to adopt. For this purpose, in the study; a simulation model was created that gives possible economic analysis results as a result of the use of RMS by using the current economic analysis results of dairy farms. For the economic analysis of dairy farms, data obtained from face-to-face surveys from 148 dairy farms were used. Assumptions used in the simulation model for comparing RMS and conventional milking systems (CMS) were 8.66% increase in milk yield, 58.46% increase in investment costs, 36.66% increase in energy consumption, 1.33% increase in feed costs and 27.84% decrease in labor input. The economic analysis of the dairy farms was made again with these new input and output values obtained. While the simulation results show that the use of RMS is a preferable investment that increases profitability for 10-60 head and 121 + head groups; it shows that it will be an investment that negatively affects profitability for the 61-120 head group. The simulation model was used by taking the average values of the data belonging to the dairy farm groups. A dairy farmer considering an RMS investment can be able to obtain a result specific to his farm if he combines the simulation model with his own economic analysis results.

A General-Equilibrium Model of Labor-Saving Technology Adoption: Theory and Evidences from Robotic Milking Systems in Idaho

Automatic milking systems (AMSs) have become increasingly common in the US in the past few years. Recent surveys from Idaho, one of the largest dairy-producing states, as well as from other states and countries, suggest that: 1. among farms adopting robotic milking systems, few are reporting less labor usage after adopting this labor-saving technology; 2. small farms rather than large farms are adopting (or more interested in adopting) robotic milking systems. In this article, we propose a series of new modeling strategies, which introduces the role of general-equilibrium effects to explain these new stylized facts. We show that: first, farms adopting labor-saving technology may, in fact, use more labor to compensate for the loss in the value of labor; second, when smaller farms experience more labor efficiency gains or value their leisure time (or off-farm income) more, they are more likely than larger farms to adopt the new technology. We contribute to the technology-adoption literature in two important ways. First, to our knowledge, this is the first article that introduces general-equilibrium effects to the technology-adoption literature. Second, this is also the first article that provides a theoretical perspective to explain the stylized facts in the adoption of robotic milking systems.

A machine learning framework to predict the next month's daily milk yield, milk composition and milking frequency for cows in a robotic dairy farm

Robotic milking systems (RMS) are increasingly utilised by modern livestock farmers because they can reduce labour costs, and they have the potential to collect data that will improve animal welfare and animal productivity through better monitoring. Sensors and devices installed in RMS enable farmers to routinely collect data on environment conditions, individual animal's behaviours, health, productivity, and milk quality. This dataset can be used to train artificial intelligence algorithms to predict trends in these variables. This study developed a machine learning framework using 5 years' behaviour, heath and productivity data from 80 cows in a robotic dairy farm. Here we demonstrate the development of a framework to automatically train models with up-to-date farm data and predict daily milk yield, composition (fat and protein content) and frequency of individual cow milking during the subsequent 28 days. A time series cross-validation was applied to simulate the application of this framework under commercial conditions and to evaluate the performance. A high accuracy of prediction (R2 > 0.90 and overall accuracy > 80%) was achieved with the models created by this framework. The practical potential of using such frameworks to enhance the management efficiency and animal welfare in robotic dairy farms is discussed.