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.

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

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

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.

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.

Forecasting Milking Efficiency of Dairy Cows Milked in an Automatic Milking System Using the Decision Tree Technique

In barns equipped with an automatic milking system, the profitability of production depends primarily on the milking efficiency of a cow (ME; kg/min) defined as cow milk yield per minute of box time. This study was carried out on 1823 Polish Holstein−Friesian cows milked by the automatic milking system (AMS) in 20 herds. Selected milking parameters recorded by the AMS were analyzed in the research. The aim of the study was to forecast ME using two statistical techniques (analysis of variance and decision trees). The results of the analysis of variance showed that the average ME was 1.67 kg/min. ME was associated with: year of AMS operation (being the highest in the first year), number of cows per robot (the highest in robots with 61−75 cows), lactation number (highest for multiparas), season of calving (the highest in spring), age at first calving (>36 months), days in milk (151−250 days) and finally, rear quarter to total milk yield ratio (the highest between 51% and 55%). The decision tree predicted that the highest ME (2.01 kg/min) corresponded with cows that produced more than 45 kg of milk per day, were milked less than four times/day, had a short teatcup attachment time (<7.65 s) and were milked in robots that had an occupancy lower than 56 cows.

Developing 'Smart' Dairy Farming Responsive to Farmers and Consumer-Citizens: A Review

Innovation has resulted in more dairy products being produced with less inputs than ever before. It has also affected how animals are raised, the structure of the sector and the nature of products produced. Not all impacts have been positive. As disruptive technologies-such as precision farming and robotics-herald significant change, it is timely to reflect on the perspectives of different actors on innovations within the sector. Drawing on a review of academic literature, this paper considers farmers' and consumer-citizens' perspectives; as expected, their diverse knowledge, interests and values surface a range of perspectives. To provide focus to the study, it examines technologies across three stages of the dairy production cycle: breeding, feeding and milking. It finds that consumer-citizen and farmer perspectives have been examined by researchers in several countries, using a variety of methods, across a range of technologies. It finds both areas of agreement and tension within and between consumer-citizen and producer cohorts. While differences in knowledge account for some variation, differences in values are also significant. The extent to which efforts can and should be put into addressing differences is raised as a point for reflection.

Impact of Heat Stress on Selected Parameters of Robotic Milking

The values of the temperature-humidity index and its influence on the performance parameters of dairy cows were monitored on four farms located in the southern part of the central Slovakia during a period of three years. The observed parameters included: the milk yield per cow per day, average milk speed and maximum milk speed. The thermal-humidity index was calculated based on a formula. The individual periods were divided according to the achieved THI. The results of dairy cows with a milk yield of 29 kg to 31 kg show that there is not a decrease in the milk yield per milking if the THI value is lower than 68. It was also found that there was a decrease in the milk yield per dairy cow in the robotic milking parlor for a THI value greater than 72. The influence of a THI value higher than 68 in these dairy cows results in a higher average milk speed, as well as a higher maximum milk speed. These two parameters are not yet in the main area of research interest. This study enriches the area with new knowledge, according to which dairy cows can show thermal stress by increasing the milk speed as well as the maximum milk speed.

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

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

Graduate Student Literature Review: Current understanding of the influence of on-farm factors on bovine raw milk and its suitability for cheesemaking

Relationships between dairy farm practices, the composition and properties of raw milk, and the quality of the resulting cheese are complex. In this review, we assess the effect of farm factors on the quality of bovine raw milk intended for cheesemaking. The literature reports several prominent farm-related factors that are closely associated with milk quality characteristics. We describe their effects on the composition and technological properties of raw milk and on the quality of the resulting cheese. Cow breed, composite genotype, and protein polymorphism all have noticeable effects on milk coagulation, cheese yield, and cheese composition. Feed and feeding strategy, dietary supplementation, housing and milking system, and seasonality of milk production also influence the composition and properties of raw milk, and the resulting cheese. The microbiota in raw milk is influenced by on-farm factors and by the production environment, and may influence the technological properties of the milk and the sensory profile of certain cheese types. Advances in research dealing with the technological properties of raw milk have undoubtedly improved understanding of how on-farm factors affect milk quality attributes, and have refuted the concept of one milk for all purposes. The specific conditions for milk production should be considered when the milk is intended for the production of cheese with unique characteristics. The scientific identification of these conditions would improve the current understanding of the complex associations between raw milk quality and farm and management factors. Future research that considers dairy landscapes within broader perspectives and develops multidimensional approaches to control the quality of raw milk intended for long-ripening cheese production is recommended.

Comparison of yield, composition and quality of milk of Polish Holstein-Friesian cows in conventional and automatic milking systems

The aim of the present study was to evaluate the changes in selected production and functional traits of Polish Holstein-Friesian cows after switching from a conventional (CMS) to an automatic milking system (AMS). The study consisted of 3398 Polish Holstein- Friesian dairy cows, from 16 herds in which CMS was changed to AMS. Cows were in their 1st (L1) or 2nd lactation (L2). The data consisted of milk yield [MY, kg], fat content [FC, %], protein content [PC, %], dry matter [DM, %], lactose content [LC, %], urea content [MU, mg/l], somatic cell count [SCC, thous./ml] and score [SCS, log]. The milking system had a significant impact on milk yield, fat, lactose, dry matter and urea contents. Regardless of lactation number, milk derived from CMS was characterised by higher values for FC, PC, DM SCC and SCS, while milk from AMS had higher MY, LC and MU. Multifactor analysis of variance also confirmed significant effect of herd, season, herd × milking system interaction on SCS in milk of cows in L1. In the studied herds change from CMS to AMS was evaluated separately for cows in L1 and L2. The transitioning from CMS to AMS resulted in the decrease of fat content in 6 L1 and 7 L2 herds, dry matter in 8 L1 and 5 L2 herds. SCS in milk also decreased in 4 L1 and 5 L2 herds. The change caused the increase of MY in 11 L1 and 9 L2 herds, lactose content in 6 L1 and 4 L2 herds and urea content in 9 L1 and 10 L2 herds. AMS may positively affect milk yield and health status, however, the change of milking system should be also accompanied by the change in herd management.

Composition and properties of bovine milk: A study from dairy farms in northern Sweden; Part I. Effect of dairy farming system

This study was part of a larger project that aimed to understand the causes for increasing variation in cheese ripening in a cheese-producing region in northern Sweden. The influence of different on-farm factors on raw milk composition and properties was investigated and is described in this paper, whereas the monthly variation in the milk quality traits during 1 yr is described in our companion paper. The dairy farming systems on a total of 42 dairy farms were characterized through a questionnaire and farm visits. Milk from farm tanks was sampled monthly over 1 yr and analyzed for quality attributes important for cheese making. On applying principal component analyses to evaluate the variation in on-farm factors, different types of farms were distinguished. Farms with loose housing and automatic milking system (AMS) or milking parlor had a higher number of lactating cows, and predominantly Swedish Holstein (SH) breed. Farms associated with tiestalls had a lower number of lactating cows and breeds other than SH. Applying principal component analyses to study the variation in composition and properties of tank milk samples from farms revealed a tendency for the formation of 2 clusters: milk from farms with AMS or a milking parlor, and milk from farms with tiestall milking. The interaction between the milking system, housing system, and breed probably contributed to this grouping. Other factors that were used in the characterization of the farming systems only showed a minor influence on raw milk quality. Despite the interaction, milk from tiestall farms with various cow breeds had higher concentrations (g/100 g of milk) of fat (4.74) and protein (3.63), and lower lactose concentrations (4.67) than milk from farms with predominantly SH cows and AMS (4.32, 3.47, and 4.74 g/100 g of milk, respectively) or a milking parlor (4.47, 3.54, and 4.79 g/100 g of milk, respectively). Higher somatic cell count (195 × 10³/mL) and lower free fatty acid concentration (0.75 mmol/100 g of fat) were observed in milk from farms with AMS than in milk from tiestall systems (150 × 10³/mL and 0.83 mmol/100 g of fat, respectively). Type of farm influenced milk gel strength, with milk from farms with predominantly SH cows showing the lowest gel strength (65.0 Pa), but not a longer rennet coagulation time. Effects of dairy farming system (e.g., dominant breed, milking system, housing, and herd size) on milk quality attributes indicate a need for further studies to evaluate the in-depth effects of farm-related factors on milk quality attributes.

Challenges and Tendencies of Automatic Milking Systems (AMS): A 20-Years Systematic Review of Literature and Patents

Over the last two decades, the dairy industry has adopted the use of Automatic Milking Systems (AMS). AMS have the potential to increase the effectiveness of the milking process and sustain animal welfare. This study assessed the state of the art of research activities on AMS through a systematic review of scientific and industrial research. The papers and patents of the last 20 years (2000-2019) were analysed to assess the research tendencies. The words appearing in title, abstract and keywords of a total of 802 documents were processed with the text mining tool. Four clusters were identified (Components, Technology, Process and Animal). For each cluster, the words frequency analysis enabled us to identify the research tendencies and gaps. The results showed that focuses of the scientific and industrial research areas complementary, with scientific papers mainly dealing with topics related to animal and process, and patents giving priority to technology and components. Both scientific and industrial research converged on some crucial objectives, such as animal welfare, process sustainability and technological development. Despite the increasing interest in animal welfare, this review highlighted that further progress is needed to meet the consumers' demand. Moreover, milk yield is still regarded as more valuable compared to milk quality. Therefore, additional effort is necessary on the latter. At the process level, some gaps have been found related to cleaning operations, necessary to improve milk quality and animal health. The use of farm data and their incorporation on herd decision support systems (DSS) appeared optimal. The results presented in this review may be used as an overall assessment useful to address future research.

Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees

The purpose of this paper was to utilize the decision trees technique to determine the factors responsible for high monthly milk yield in Polish Holstein-Friesian cows from 27 herds equipped with milking robots. The applied statistical method-the decision tree technique-showed that the most important factors responsible for monthly milk yield of dairy cows using robots were, in descending order of importance: milking frequency, lactation number, month of milking, and type of lying stall. At the same time, it has been ascertained that the highest monthly milk yield (47.24 kg) can be expected from multiparous cows kept in barns with a deep bedding that were milked more frequently than three times per day. On the other hand, the lowest milk production (13.56 kg) was observed among dairy cows milked less frequently than two times a day, with an average number of milked quarters lower than 3.97. The application of the decision trees technique allows a breeder to select appropriate levels of environmental factors and parameters that will help to ensure maximized milk production.

Review: Sensor techniques in ruminants: more than fitness trackers

In this position paper, I shall summarise the current status of sensor technologies in ruminant livestock farming with emphasis on dairy cattle, outline the case for why I believe that sensor technologies could revolutionise global dairy farming in a positive way, describe the significant barriers that exist if that goal is to be achieved and highlight the benefits to animal wellbeing, profitability and sustainability that could result if the technologies are implemented to a significant extent. I shall not provide a comprehensive review of the sensor technology literature since that has been done before, but I intend to provide a sensible amount of background information and data that will allow the reader to obtain a picture not only of today's sensor usage but, more importantly, the possible future direction of dairy animal-oriented sensor technologies, and I shall substantiate my claims and conclusions with relevant literature.

The milking frequency of primiparous cows in their early stage of lactation and its impact on milking performance

An automatic milking system allows cows to present their full production capability by not limiting them to a specific time when the milking occurs or a fix number of milkings per day. The beginning of the first lactation is a key point in terms of subsequent milk production. The aim of the present study was to indicate the relationship between the milking frequency of primiparous cows during the first month of lactation and their subsequent milk performance. Material of the study consisted of 25 Polish herds of Holstein–Friesian dairy cattle. All cows were milked with the use of an automatic milking system. Animals were divided into five groups, depending on the milking frequency in the first month after calving (MFF). The collected data were statistically processed using the multifactorial ANOVA. The best milk and milking parameters characterised primiparous cows, for which the average number of milkings per day was at the level of 3–3.5 or above, this group did not have a preferred time for their milking. This group of cows milked more frequently during the first month of lactation (MFF5) and had the highest milk yield (MY) and milking duration. The highest culling percentage (57.77%) was noted within the group of primiparous cows with the lowest milking frequency during the first month of lactation (MFF1). MFF5 animals maintained better milk and milking parameters in all months of lactation than did those in the other groups. Older animals, that calved after the 28th month of life, and those that calved during warmer seasons, showed the tendency to have a lower milking frequency and poorer milk and milking parameters. The findings obtained in the present study are interesting in terms of their potential use, because they show that frequent milking during the first month after calving corresponds to a better overall MY during that lactation. Hopefully, by promoting frequent milkings at the beginning of lactation, farmer may increase the overall lactation MY.

Transitioning from conventional to automatic milking: Effects on the human-animal relationship

There are several differences in how an automatic milking system (AMS; milking equipment not requiring human intervention for the milk harvesting process) and a conventional milking system (CMS) are managed, where the effect of milking system type on the human-animal relationship has remained unexplored. A survey and observations from 5 Australian dairy farms transitioning from CMS to AMS were taken twice, 1 yr apart, before and after transition to an AMS. The farmers completed a survey and had all farmer-cow interactions documented for 3 d. In addition, a random selection of lactating cows had their avoidance distance (the distance at which they move away from an approaching person) recorded and were involved in a handling test during both visits. The survey findings indicated that basic management practices remained mostly unchanged, whereas records of farmer-cow interactions showed 4 out of 5 farms had less interaction time after AMS transition. This was caused by a reduction in milk harvesting tasks, where a small portion of this time was re-invested into time that farmers spent around the cows without directly interacting with them and into tasks involving close cow contact. Overall, an approximate 27% decline was observed in avoidance distances of cows from an AMS compared with the CMS. A handling test was performed on 4 of the 5 farms before and after AMS transition, where the farmers were asked to move a selection of cows through a gate one at a time. In the AMS more vocal effort was required to move the cows, and the cows responded with a reduced occurrence of running past the farmer and reduced occurrence of slipping in an attempt to avoid the farmers compared with the CMS. Overall, results show that farmers spent less time interacting with cows in the AMS, and that cows were less fearful around people as seen by reduced avoidance distances and reduced stress responses to close handling.

Large-scale cross-sectional study of relationships between somatic cell count and milking-time test results in different milking systems

Milking-time testing (MTT) is a method for evaluating the vacuum conditions in the teatcup during milking. The purpose is to evaluate the possible impact of the milking and milking equipment on udder health and milk quality. The method is commonly implemented by herd health advisory services, but results are interpreted empirically due to lack of scientific documentation on relationships between MTT result variables and objective measures of udder health. The current study was conducted to increase our understanding of associations between cow-level differences in composite milk somatic cell count (CMSCC) and MTT results in dairy cows milked in 3 different milking systems; automatic milking systems (AMS), milking parlors, and pipeline milking systems. Data from 7069 cows (predominantly Norwegian Red breed) in 1009 herds were used in a cross-sectional study. Multilevel linear regression models with a random intercept at herd level were used to describe relationships between CMSCC (on logarithmic scale) and the following MTT explanatory variables: average vacuum level in the short milk tube and mouthpiece chamber in the main milking and overmilking periods, the duration of these two periods, and vacuum stability, measured by sudden vacuum drops in the short milk tube. The models were corrected for the herd effect, mastitis history and differences in milk yield, lactation stage and parity between cows. Separate models were run for AMS, milking parlors, and pipeline milking systems, because this approach allowed for comparison between systems and for evaluation of the herd effect independently of milking system. The models described 8-10 % of the variation in CMSCC, indicating that MTT could only explain a relatively small proportion of a large total variation in CMSCC. In most observations, vacuum levels in the short milk tube during main milking were within the range recommended by the International Organization for Standardization. The results from our multivariable models showed decreasing CMSCC with increasing vacuum level in the short milk tube during the main milking period in AMS and milking parlors. Similarly, decreasing CMSCC was also associated with increasing duration of the main milking period in all 3 systems. These relationships are important for the interpretation of MTT results under practical conditions; finding high vacuum levels and long milking durations in a MTT is not associated with elevated CMSCC. In AMS herds, we also found indications that the relationships were different for cows where a case of mastitis had been treated before the MTT.

Influência da profundidade do úbere na limpeza dos tetos e na saúde da glândula mamária em ordenha robótica

RESUMO O objetivo deste trabalho foi avaliar a influência da profundidade do úbere sobre a limpeza de tetos e a saúde da glândula mamária. O experimento foi desenvolvido no período de março a junho de 2014, em Castro, PR. Foram utilizadas 20 vacas da raça Holandesa confinadas em free stall e ordenhadas em sistema de ordenha robotizada (SOR). As vacas foram divididas em quatro grupos com cinco animais cada (duas primíparas e três multíparas), com base na distância do piso do úbere em relação ao jarrete (úbere profundo, normal, pequeno e raso). Os dados, registrados eletronicamente, foram coletados mensalmente, por quatro meses, referentes à semana do controle leiteiro oficial. Para avaliar a efetividade da limpeza dos tetos, foram feitos swabs de dois tetos contralaterais (anterior direito e posterior esquerdo) antes e após a limpeza automática, bem como foram esfregadas toalhas umedecidas, uma vez, ao redor dos outros tetos. Uma amostra de leite foi coletada por vaca para determinar a contagem de células somáticas (CCS). Na análise multivariada, foi observada relação da profundidade do úbere com a limpeza de tetos avaliada por meio do escore de limpeza de tetos com toalhas umedecidas (ELTT). Os úberes levemente acima do jarrete (normal) apresentaram menor CCS e contagem bacteriana total (CBT) dos tetos. Os úberes rasos apresentaram maior diferença no ELTT. As vacas com úberes profundo e raso apresentaram menor efetividade na limpeza de tetos e na saúde da glândula mamária. Vacas com úbere normal apresentaram conformação e sanidade da glândula mamária e contaminação de tetos mais adequadas à ordenha robótica. Em fazendas que pretendem introduzir o SOR, é recomendado selecionar vacas com úbere com profundidade um pouco acima do jarrete. O impacto do SOR na CBT dos tetos está relacionado à condição de limpeza deles na pré-ordenha e das instalações. Portanto, a condição de ambiência das vacas é fundamental para a saúde da glândula mamária e a obtenção de um leite com alta qualidade.

Milk-flow data collected routinely in an automatic milking system: an alternative to milking-time testing in the management of teat-end condition?

Background

Having a poor teat-end condition is associated with increased mastitis risk, hence avoiding milking machine settings that have a negative effect on teat-end condition is important for successful dairy production. Milking-time testing (MTT) can be used in the evaluation of vacuum conditions during milking, but the method is less suited for herds using automatic milking systems (AMS) and relationships with teat end condition is poorly described. This study aimed to increase knowledge on interpretation of MTT in AMS and to assess whether milk-flow data obtained routinely by an AMS can be useful for the management of teat-end health. A cross-sectional study, including 251 teats of 79 Norwegian Red cows milked by AMS was performed in the research herd of the Norwegian University of Life Sciences. The following MTT variables were obtained at teat level: Average vacuum level in the short milk tube during main milking (MTVAC), average vacuum in the mouthpiece chamber during main milking and overmilking, teat compression intensity (COMPR) and overmilking time. Average and peak milk flow rates were obtained at quarter level from the AMS software. Teat-end callosity thickness and roughness was registered, and teat dimensions; length, and width at apex and base, were measured. Interrelationships among variables obtained by MTT, quarter milk flow variables, and teat dimensions were described. Associations between these variables and teat-end callosity thickness and roughness, were investigated.

Results

Principal component analysis showed clusters of strongly related variables. There was a strong negative relationship between MTVAC and average milk flow rate. The variables MTVAC, COMPR and average and peak milk flow rate were associated with both thickness and roughness of the callosity ring.

Conclusions

Quarter milk flow rate obtained directly from the AMS software was useful in assessing associations between milking machine function and teat-end condition; low average milk flow rates were associated with a higher likelihood of the teat having a thickened or roughened teat-end callosity ring. Since information on milk flow rate is readily available from the herd management system, this information might be used when evaluating causes for impaired teat-end condition in AMS.

The effect of lactation number, stage, length, and milking frequency on milk yield in Korean Holstein dairy cows using automatic milking system

Objective

The aim of the current study was to describe the relationship between milk yield and lactation number, stage, length and milking frequency in Korean Holstein dairy cows using an automatic milking system (AMS).

Methods

The original data set consisted of observations from April to October 2016 of 780 Holstein cows, with a total of 10,751 milkings. Each time a cow was milked by an AMS during the 24 h, the AMS management system recorded identification numbers of the AMS unit, the cow being milking, date and time of the milking, and milk yield (kg) as measured by the milk meters installed on each AMS unit, date and time of the lactation, lactation stage, milking frequency (NoM). Lactation stage is defined as the number of days milking per cows per lactation. Milk yield was calculated per udder quarter in the AMS and was added to 1 record per cow and trait for each milking. Milking frequency was measured the number of milkings per cow per 24 hour.

Results

From the study results, a significant relationship was found between the milk yield and lactation number (p<0.001), with the maximum milk yield occurring in the third lactation cows. We recorded the highest milk yield, in a greater lactation length period of early stage (55 to 90 days) at a 4× milking frequency/d, and the lowest milk yield was observed in the later stage (>201 days) of cows. Also, milking frequency had a significant influence on milk yield (p<0.001) in Korean Holstein cows using AMS.

Conclusion

Detailed knowledge of these factors such as lactation number, stage, length, and milking frequency associated with increasing milk yield using AMS will help guide future recommendations to producers for maximizing milk yield in Korean Dairy industries.

Occupational Health and Safety of Finnish Dairy Farmers Using Automatic Milking Systems

INTRODUCTION

Conventional pipeline and parlor milking expose dairy farmers and workers to adverse health outcomes. In recent years, automatic milking systems (AMS) have gained much popularity in Finland, but the changes in working conditions when changing to AMS are not well known. The aim of this study was to investigate the occupational health and safety risks in using AMS, compared to conventional milking systems (CMS).

METHODS

An anonymous online survey was sent to each Finnish dairy farm with an AMS in 2014. Only those dairy farmers with prior work experience in CMS were included in the final analysis consisting of frequency distributions and descriptive statistics.

RESULTS

We received 228 usable responses (131 male and 97 female; 25.2% response rate). The majority of the participants found that AMS had brought flexibility to the organization of farm work, and it had increased leisure time, quality of life, productivity of dairy work, and the attractiveness of dairy farming among the younger generation. In addition, AMS reduced the perceived physical strain on the musculoskeletal system as well as the risk of occupational injuries and diseases, compared to CMS. However, working in close proximity to the cattle, particularly training of heifers to use the AMS, was regarded as a high-risk work task. In addition, the daily cleaning of the AMS and manual handling of rejected milk were regarded as physically demanding. The majority of the participants stated that mental stress caused by the monotonous, repetitive, paced, and hurried work had declined after changing to AMS. However, many indicated increased mental stress because of the demanding management of the AMS. Nightly alarms caused by the AMS, lack of adequately skilled hired labor or farm relief workers, and the 24/7 standby for the AMS were issues that also caused mental stress.

CONCLUSION

Based on this study, AMS may have significant potential in the prevention of adverse health outcomes in milking of dairy cows. In addition, AMS may improve the productivity of dairy work and sustainability of dairy production. However, certain characteristics of the AMS require further attention with regard to occupational health and safety risks.

The effect of pulsation ratio on teat condition, milk somatic cell count and productivity in dairy cows in automatic milking

The pulsation ratio of a milking machine affects milk flow and milking time, and has also been reported to influence teat condition and milk somatic cell count (SCC). However, most studies comparing pulsation ratios have been performed on conventional cluster milking (whole-udder level), where effects such as deteriorated teat end condition and increased milk SCC are likely to be caused by over-milking on teats that are emptied faster than the other teats. When the teat cups are detached from each udder quarter separately which can be done in automatic milking systems (AMS), the risk of over-milking, especially in front teats, may be significantly reduced. This study investigated the effects of pulsation ratio on teat end condition, milk SCC, milk yield, milking time and milk flow in an automatic milking system where each udder quarter is milked separately. In total, 356 cows on five commercial farms were included in a split-udder design experiment comparing three pulsation ratios (60:40, 70:30 and 75:25) with the standard pulsation ratio (65:35) during 6 weeks. Pulsation rate was 60 cycles/min and vacuum level 46 kPa. The 70:30 and 75:25 ratios increased peak and average milk flow and the machine-on time was shorter with 75:25, while both peak and average milk flows were lower and machine-on time was longer with the 60:40 ratio. No negative effects on teat condition or milk SCC were observed with any of the pulsation ratios applied during the study. Thus it is possible that increased pulsation ratio can be used to increase milking efficiency in AMS where quarter milking is applied.

Changes in milking parameters with robotic milking

Abstract. The aim of this present study is to describe changes occurring during the milking of cows in various periods following the introduction of an AMS (automatic milking system). The following cow milking parameters were analysed: milkings per cow per day, milking yield, milking speed and milking duration. An increase in milk yield in AMS barns has been found to be possible, but it is affected by a number of factors related to cow milking performance. Milk yield was observed to gradually grow with time after the installation of the robots. Older cows in their third and fourth lactations achieved higher milking parameter values as compared to cows in their first and second lactations. The average milk yield for the whole period was on a similar level, but, due to the fact that the duration of lactation in herd B was more than 100 days longer, that herd achieved a higher milk yield. The use of AMSs in barns enables farmers to monitor cow performance traits and study the relationships between them; farmers should try to select for traits ensuring high performance and directly related to milk yield. This study found a positive relationship between milking duration and milk yield. On the other hand, a highly negative relationship was found between milking duration and milking speed, which means that these parameters should be closely monitored. This study found that the optimal number of milkings per cow per day was in the range of 2.6 to 2.8 milkings a day with a 2.6 kg min−1 milking speed.

Technical note: Assessment of milk temperature measured by automatic milking systems as an indicator of body temperature and fever in dairy cows

The objective of this study was to evaluate whether milk temperature (MT) measured by automatic milking system (AMS) is a reliable indicator of body temperature of dairy cows and whether cows with fever could be detected. Data loggers (Minilog 8, Vemco Ltd., Halifax, NS, Canada) measuring body temperature were inserted for 7 ± 1 d into the vaginal cavity of 31 dairy cows and programmed to take 1 reading/min. Milk temperature was recorded at each milking event by the AMS, and values from the vaginal loggers were paired with the corresponding MT. The correlation (r) between vaginal temperature (VT) and MT was 0.52. Vaginal temperature was higher (39.1 ± 0.4°C) than MT (38.6 ± 0.7°C) with a mean difference of 0.5 ± 0.6°C. The ability of MT to identify cows with fever was assessed using 2 approaches. In the first approach, VT could indicate fever at any time of the day, whereas MT could display fever only during the milking events of a given day. Different definitions of fever based on thresholds of VT and duration exceeding these thresholds were constructed. Different thresholds of MT were tested to distinguish between cows with and without fever. The combination of 39.0°C as a threshold for MT and 39.5°C for at least 2h/d as a threshold for VT resulted in the highest combination of sensitivity (0.65) and specificity (0.65). In the second approach, we evaluated whether MT could identify cows with fever at a given milking event. A threshold of MT >38.7°C delivered the best combination of sensitivity (0.77) and specificity (0.66) when fever was defined as VT ≥39.5°C. Therefore, MT measured by AMS can be indicative of fever in dairy cows to a limited extent.

Effect of low light intensity at night on cow traffic in automatic milking systems

Several studies have shown benefits of long-day (16 h) photoperiod in lactating dairy cows, but have not identified a suitable light intensity for the dark hours. It is known that the locomotion pattern of dairy cows is altered at low light intensities and this may translate to reduced cow traffic and milking frequency, which would have a negative impact on system productivity. However, it is also recognised that a significant disturbance of rest may have a negative impact on the health and productivity of high-yielding dairy cows. This study examined the effect of three different night-time light intensities (LOW: 11 ± 3, MED: 33 ± 1 and HIGH: 74 ± 6 lx) on number of gate passages, milking frequency and milk yield in dairy cows in automatic milking systems. The study was conducted in Sweden during the winter of 2012–13 and the treatments were applied in a crossover design to three herds with an automatic milking system. Minimum day time light intensity was 158 lx. Data on gate passages, milking frequency and milk yield for 172 ± 49 (mean ± s.d.) cows during the last 22 days of each 34-day study period were analysed for treatment differences and differences in daily distribution over 24 h, during day time and night time. Light intensity did not affect total number of gate passages per 24-h period and cow, but number of gate passages per hour and cow was in all treatments lower during night time than during day time. Milking frequency was increased in MED compared with both HIGH and LOW (P < 0.05). Milk yield decreased with reduced light intensity, and differed significantly between HIGH and LOW treatments, 45 ± 1 kg and 44 ± 1 kg, respectively (P < 0.001). Our conclusion is that reducing light intensity to 11 lx at night time does not affect cows’ general activity as gate passages remained the same for all treatments. However, milk yield decreased with reduced light intensity, which might be related to a lower feed intake. We argue that providing night light for dairy cows, as required by many welfare acts, might be related to production level rather than welfare aspects and that the recommendations should be revised.

Invited review: sensors to support health management on dairy farms

Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.